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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.06494">arXiv:2412.06494</a> <span> [<a href="https://arxiv.org/pdf/2412.06494">pdf</a>, <a href="https://arxiv.org/format/2412.06494">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cremonesi%2C+F">Francesco Cremonesi</a>, <a href="/search/cs?searchtype=author&query=Innocenti%2C+L">Lucia Innocenti</a>, <a href="/search/cs?searchtype=author&query=Ourselin%2C+S">Sebastien Ourselin</a>, <a href="/search/cs?searchtype=author&query=Goh%2C+V">Vicky Goh</a>, <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Michela Antonelli</a>, <a href="/search/cs?searchtype=author&query=Lorenzi%2C+M">Marco Lorenzi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.06494v1-abstract-short" style="display: inline;"> Background. Federated learning (FL) has gained wide popularity as a collaborative learning paradigm enabling collaborative AI in sensitive healthcare applications. Nevertheless, the practical implementation of FL presents technical and organizational challenges, as it generally requires complex communication infrastructures. In this context, consensus-based learning (CBL) may represent a promising… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.06494v1-abstract-full').style.display = 'inline'; document.getElementById('2412.06494v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.06494v1-abstract-full" style="display: none;"> Background. Federated learning (FL) has gained wide popularity as a collaborative learning paradigm enabling collaborative AI in sensitive healthcare applications. Nevertheless, the practical implementation of FL presents technical and organizational challenges, as it generally requires complex communication infrastructures. In this context, consensus-based learning (CBL) may represent a promising collaborative learning alternative, thanks to the ability of combining local knowledge into a federated decision system, while potentially reducing deployment overhead. Methods. In this work we propose an extensive benchmark of the accuracy and cost-effectiveness of a panel of FL and CBL methods in a wide range of collaborative medical data analysis scenarios. The benchmark includes 7 different medical datasets, encompassing 3 machine learning tasks, 8 different data modalities, and multi-centric settings involving 3 to 23 clients. Findings. Our results reveal that CBL is a cost-effective alternative to FL. When compared across the panel of medical dataset in the considered benchmark, CBL methods provide equivalent accuracy to the one achieved by FL.Nonetheless, CBL significantly reduces training time and communication cost (resp. 15 fold and 60 fold decrease) (p < 0.05). Interpretation. This study opens a novel perspective on the deployment of collaborative AI in real-world applications, whereas the adoption of cost-effective methods is instrumental to achieve sustainability and democratisation of AI by alleviating the need for extensive computational resources. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.06494v1-abstract-full').style.display = 'none'; document.getElementById('2412.06494v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.17763">arXiv:2409.17763</a> <span> [<a href="https://arxiv.org/pdf/2409.17763">pdf</a>, <a href="https://arxiv.org/format/2409.17763">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Confidence intervals uncovered: Are we ready for real-world medical imaging AI? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Christodoulou%2C+E">Evangelia Christodoulou</a>, <a href="/search/cs?searchtype=author&query=Reinke%2C+A">Annika Reinke</a>, <a href="/search/cs?searchtype=author&query=Houhou%2C+R">Rola Houhou</a>, <a href="/search/cs?searchtype=author&query=Kalinowski%2C+P">Piotr Kalinowski</a>, <a href="/search/cs?searchtype=author&query=Erkan%2C+S">Selen Erkan</a>, <a href="/search/cs?searchtype=author&query=Sudre%2C+C+H">Carole H. Sudre</a>, <a href="/search/cs?searchtype=author&query=Burgos%2C+N">Ninon Burgos</a>, <a href="/search/cs?searchtype=author&query=Boutaj%2C+S">Sofi猫ne Boutaj</a>, <a href="/search/cs?searchtype=author&query=Loizillon%2C+S">Sophie Loizillon</a>, <a href="/search/cs?searchtype=author&query=Solal%2C+M">Ma毛lys Solal</a>, <a href="/search/cs?searchtype=author&query=Rieke%2C+N">Nicola Rieke</a>, <a href="/search/cs?searchtype=author&query=Cheplygina%2C+V">Veronika Cheplygina</a>, <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Michela Antonelli</a>, <a href="/search/cs?searchtype=author&query=Mayer%2C+L+D">Leon D. Mayer</a>, <a href="/search/cs?searchtype=author&query=Tizabi%2C+M+D">Minu D. Tizabi</a>, <a href="/search/cs?searchtype=author&query=Cardoso%2C+M+J">M. Jorge Cardoso</a>, <a href="/search/cs?searchtype=author&query=Simpson%2C+A">Amber Simpson</a>, <a href="/search/cs?searchtype=author&query=J%C3%A4ger%2C+P+F">Paul F. J盲ger</a>, <a href="/search/cs?searchtype=author&query=Kopp-Schneider%2C+A">Annette Kopp-Schneider</a>, <a href="/search/cs?searchtype=author&query=Varoquaux%2C+G">Ga毛l Varoquaux</a>, <a href="/search/cs?searchtype=author&query=Colliot%2C+O">Olivier Colliot</a>, <a href="/search/cs?searchtype=author&query=Maier-Hein%2C+L">Lena Maier-Hein</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.17763v2-abstract-short" style="display: inline;"> Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. In this paper, we argue that this common practice is often a misleading simplification as it ignores performance variability. Our contribution is three… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17763v2-abstract-full').style.display = 'inline'; document.getElementById('2409.17763v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.17763v2-abstract-full" style="display: none;"> Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. In this paper, we argue that this common practice is often a misleading simplification as it ignores performance variability. Our contribution is threefold. (1) Analyzing all MICCAI segmentation papers (n = 221) published in 2023, we first observe that more than 50% of papers do not assess performance variability at all. Moreover, only one (0.5%) paper reported confidence intervals (CIs) for model performance. (2) To address the reporting bottleneck, we show that the unreported standard deviation (SD) in segmentation papers can be approximated by a second-order polynomial function of the mean Dice similarity coefficient (DSC). Based on external validation data from 56 previous MICCAI challenges, we demonstrate that this approximation can accurately reconstruct the CI of a method using information provided in publications. (3) Finally, we reconstructed 95% CIs around the mean DSC of MICCAI 2023 segmentation papers. The median CI width was 0.03 which is three times larger than the median performance gap between the first and second ranked method. For more than 60% of papers, the mean performance of the second-ranked method was within the CI of the first-ranked method. We conclude that current publications typically do not provide sufficient evidence to support which models could potentially be translated into clinical practice. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17763v2-abstract-full').style.display = 'none'; document.getElementById('2409.17763v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Paper accepted at MICCAI 2024 conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.11999">arXiv:2409.11999</a> <span> [<a href="https://arxiv.org/pdf/2409.11999">pdf</a>, <a href="https://arxiv.org/ps/2409.11999">ps</a>, <a href="https://arxiv.org/format/2409.11999">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> </div> </div> <p class="title is-5 mathjax"> On Randomized Computational Models and Complexity Classes: a Historical Overview </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Melissa Antonelli</a>, <a href="/search/cs?searchtype=author&query=Lago%2C+U+D">Ugo Dal Lago</a>, <a href="/search/cs?searchtype=author&query=Pistone%2C+P">Paolo Pistone</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.11999v1-abstract-short" style="display: inline;"> Since their appearance in the 1950s, computational models capable of performing probabilistic choices have received wide attention and are nowadays pervasive in almost every areas of computer science. Their development was also inextricably linked with inquiries about computation power and resource issues. Although most crucial notions in the field are well-known, the related terminology is someti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11999v1-abstract-full').style.display = 'inline'; document.getElementById('2409.11999v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.11999v1-abstract-full" style="display: none;"> Since their appearance in the 1950s, computational models capable of performing probabilistic choices have received wide attention and are nowadays pervasive in almost every areas of computer science. Their development was also inextricably linked with inquiries about computation power and resource issues. Although most crucial notions in the field are well-known, the related terminology is sometimes imprecise or misleading. The present work aims to clarify the core features and main differences between machines and classes developed in relation to randomized computation. To do so, we compare the modern definitions with original ones, recalling the context in which they first appeared, and investigate the relations linking probabilistic and counting models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11999v1-abstract-full').style.display = 'none'; document.getElementById('2409.11999v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.15003">arXiv:2311.15003</a> <span> [<a href="https://arxiv.org/pdf/2311.15003">pdf</a>, <a href="https://arxiv.org/ps/2311.15003">ps</a>, <a href="https://arxiv.org/format/2311.15003">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Logic">math.LO</span> </div> </div> <p class="title is-5 mathjax"> Enumerating Error Bounded Polytime Algorithms Through Arithmetical Theories </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Melissa Antonelli</a>, <a href="/search/cs?searchtype=author&query=Lago%2C+U+D">Ugo Dal Lago</a>, <a href="/search/cs?searchtype=author&query=Davoli%2C+D">Davide Davoli</a>, <a href="/search/cs?searchtype=author&query=Oitavem%2C+I">Isabel Oitavem</a>, <a href="/search/cs?searchtype=author&query=Pistone%2C+P">Paolo Pistone</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.15003v1-abstract-short" style="display: inline;"> We consider a minimal extension of the language of arithmetic, such that the bounded formulas provably total in a suitably-defined theory 脿 la Buss (expressed in this new language) precisely capture polytime random functions. Then, we provide two new characterizations of the semantic class BPP obtained by internalizing the error-bound check within a logical system: the first relies on measure-sens… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.15003v1-abstract-full').style.display = 'inline'; document.getElementById('2311.15003v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.15003v1-abstract-full" style="display: none;"> We consider a minimal extension of the language of arithmetic, such that the bounded formulas provably total in a suitably-defined theory 脿 la Buss (expressed in this new language) precisely capture polytime random functions. Then, we provide two new characterizations of the semantic class BPP obtained by internalizing the error-bound check within a logical system: the first relies on measure-sensitive quantifiers, while the second is based on standard first-order quantification. This leads us to introduce a family of effectively enumerable subclasses of BPP, called BPP_T and consisting of languages captured by those probabilistic Turing machines whose underlying error can be proved bounded in the theory T. As a paradigmatic example of this approach, we establish that polynomial identity testing is in BPP_T where T=$\mathrm{I}螖_0+\mathrm{Exp}$ is a well-studied theory based on bounded induction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.15003v1-abstract-full').style.display = 'none'; document.getElementById('2311.15003v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> F.4.1; F.1.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.17097">arXiv:2309.17097</a> <span> [<a href="https://arxiv.org/pdf/2309.17097">pdf</a>, <a href="https://arxiv.org/format/2309.17097">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Innocenti%2C+L">Lucia Innocenti</a>, <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Michela Antonelli</a>, <a href="/search/cs?searchtype=author&query=Cremonesi%2C+F">Francesco Cremonesi</a>, <a href="/search/cs?searchtype=author&query=Sarhan%2C+K">Kenaan Sarhan</a>, <a href="/search/cs?searchtype=author&query=Granados%2C+A">Alejandro Granados</a>, <a href="/search/cs?searchtype=author&query=Goh%2C+V">Vicky Goh</a>, <a href="/search/cs?searchtype=author&query=Ourselin%2C+S">Sebastien Ourselin</a>, <a href="/search/cs?searchtype=author&query=Lorenzi%2C+M">Marco Lorenzi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.17097v2-abstract-short" style="display: inline;"> Healthcare data is often split into medium/small-sized collections across multiple hospitals and access to it is encumbered by privacy regulations. This brings difficulties to use them for the development of machine learning and deep learning models, which are known to be data-hungry. One way to overcome this limitation is to use collaborative learning (CL) methods, which allow hospitals to work c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.17097v2-abstract-full').style.display = 'inline'; document.getElementById('2309.17097v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.17097v2-abstract-full" style="display: none;"> Healthcare data is often split into medium/small-sized collections across multiple hospitals and access to it is encumbered by privacy regulations. This brings difficulties to use them for the development of machine learning and deep learning models, which are known to be data-hungry. One way to overcome this limitation is to use collaborative learning (CL) methods, which allow hospitals to work collaboratively to solve a task, without the need to explicitly share local data. In this paper, we address a prostate segmentation problem from MRI in a collaborative scenario by comparing two different approaches: federated learning (FL) and consensus-based methods (CBM). To the best of our knowledge, this is the first work in which CBM, such as label fusion techniques, are used to solve a problem of collaborative learning. In this setting, CBM combine predictions from locally trained models to obtain a federated strong learner with ideally improved robustness and predictive variance properties. Our experiments show that, in the considered practical scenario, CBMs provide equal or better results than FL, while being highly cost-effective. Our results demonstrate that the consensus paradigm may represent a valid alternative to FL for typical training tasks in medical imaging. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.17097v2-abstract-full').style.display = 'none'; document.getElementById('2309.17097v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.10655">arXiv:2305.10655</a> <span> [<a href="https://arxiv.org/pdf/2305.10655">pdf</a>, <a href="https://arxiv.org/format/2305.10655">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-3-031-17027-0_2">10.1007/978-3-031-17027-0_2 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Diaz-Pinto%2C+A">Andres Diaz-Pinto</a>, <a href="/search/cs?searchtype=author&query=Mehta%2C+P">Pritesh Mehta</a>, <a href="/search/cs?searchtype=author&query=Alle%2C+S">Sachidanand Alle</a>, <a href="/search/cs?searchtype=author&query=Asad%2C+M">Muhammad Asad</a>, <a href="/search/cs?searchtype=author&query=Brown%2C+R">Richard Brown</a>, <a href="/search/cs?searchtype=author&query=Nath%2C+V">Vishwesh Nath</a>, <a href="/search/cs?searchtype=author&query=Ihsani%2C+A">Alvin Ihsani</a>, <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Michela Antonelli</a>, <a href="/search/cs?searchtype=author&query=Palkovics%2C+D">Daniel Palkovics</a>, <a href="/search/cs?searchtype=author&query=Pinter%2C+C">Csaba Pinter</a>, <a href="/search/cs?searchtype=author&query=Alkalay%2C+R">Ron Alkalay</a>, <a href="/search/cs?searchtype=author&query=Pieper%2C+S">Steve Pieper</a>, <a href="/search/cs?searchtype=author&query=Roth%2C+H+R">Holger R. Roth</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+D">Daguang Xu</a>, <a href="/search/cs?searchtype=author&query=Dogra%2C+P">Prerna Dogra</a>, <a href="/search/cs?searchtype=author&query=Vercauteren%2C+T">Tom Vercauteren</a>, <a href="/search/cs?searchtype=author&query=Feng%2C+A">Andrew Feng</a>, <a href="/search/cs?searchtype=author&query=Quraini%2C+A">Abood Quraini</a>, <a href="/search/cs?searchtype=author&query=Ourselin%2C+S">Sebastien Ourselin</a>, <a href="/search/cs?searchtype=author&query=Cardoso%2C+M+J">M. Jorge Cardoso</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.10655v1-abstract-short" style="display: inline;"> Automatic segmentation of medical images is a key step for diagnostic and interventional tasks. However, achieving this requires large amounts of annotated volumes, which can be tedious and time-consuming task for expert annotators. In this paper, we introduce DeepEdit, a deep learning-based method for volumetric medical image annotation, that allows automatic and semi-automatic segmentation, and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10655v1-abstract-full').style.display = 'inline'; document.getElementById('2305.10655v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.10655v1-abstract-full" style="display: none;"> Automatic segmentation of medical images is a key step for diagnostic and interventional tasks. However, achieving this requires large amounts of annotated volumes, which can be tedious and time-consuming task for expert annotators. In this paper, we introduce DeepEdit, a deep learning-based method for volumetric medical image annotation, that allows automatic and semi-automatic segmentation, and click-based refinement. DeepEdit combines the power of two methods: a non-interactive (i.e. automatic segmentation using nnU-Net, UNET or UNETR) and an interactive segmentation method (i.e. DeepGrow), into a single deep learning model. It allows easy integration of uncertainty-based ranking strategies (i.e. aleatoric and epistemic uncertainty computation) and active learning. We propose and implement a method for training DeepEdit by using standard training combined with user interaction simulation. Once trained, DeepEdit allows clinicians to quickly segment their datasets by using the algorithm in auto segmentation mode or by providing clicks via a user interface (i.e. 3D Slicer, OHIF). We show the value of DeepEdit through evaluation on the PROSTATEx dataset for prostate/prostatic lesions and the Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) dataset for abdominal CT segmentation, using state-of-the-art network architectures as baseline for comparison. DeepEdit could reduce the time and effort annotating 3D medical images compared to DeepGrow alone. Source code is available at https://github.com/Project-MONAI/MONAILabel <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10655v1-abstract-full').style.display = 'none'; document.getElementById('2305.10655v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.01790">arXiv:2302.01790</a> <span> [<a href="https://arxiv.org/pdf/2302.01790">pdf</a>, <a href="https://arxiv.org/format/2302.01790">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41592-023-02150-0">10.1038/s41592-023-02150-0 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Understanding metric-related pitfalls in image analysis validation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Reinke%2C+A">Annika Reinke</a>, <a href="/search/cs?searchtype=author&query=Tizabi%2C+M+D">Minu D. Tizabi</a>, <a href="/search/cs?searchtype=author&query=Baumgartner%2C+M">Michael Baumgartner</a>, <a href="/search/cs?searchtype=author&query=Eisenmann%2C+M">Matthias Eisenmann</a>, <a href="/search/cs?searchtype=author&query=Heckmann-N%C3%B6tzel%2C+D">Doreen Heckmann-N枚tzel</a>, <a href="/search/cs?searchtype=author&query=Kavur%2C+A+E">A. Emre Kavur</a>, <a href="/search/cs?searchtype=author&query=R%C3%A4dsch%2C+T">Tim R盲dsch</a>, <a href="/search/cs?searchtype=author&query=Sudre%2C+C+H">Carole H. Sudre</a>, <a href="/search/cs?searchtype=author&query=Acion%2C+L">Laura Acion</a>, <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Michela Antonelli</a>, <a href="/search/cs?searchtype=author&query=Arbel%2C+T">Tal Arbel</a>, <a href="/search/cs?searchtype=author&query=Bakas%2C+S">Spyridon Bakas</a>, <a href="/search/cs?searchtype=author&query=Benis%2C+A">Arriel Benis</a>, <a href="/search/cs?searchtype=author&query=Blaschko%2C+M">Matthew Blaschko</a>, <a href="/search/cs?searchtype=author&query=Buettner%2C+F">Florian Buettner</a>, <a href="/search/cs?searchtype=author&query=Cardoso%2C+M+J">M. Jorge Cardoso</a>, <a href="/search/cs?searchtype=author&query=Cheplygina%2C+V">Veronika Cheplygina</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J">Jianxu Chen</a>, <a href="/search/cs?searchtype=author&query=Christodoulou%2C+E">Evangelia Christodoulou</a>, <a href="/search/cs?searchtype=author&query=Cimini%2C+B+A">Beth A. Cimini</a>, <a href="/search/cs?searchtype=author&query=Collins%2C+G+S">Gary S. Collins</a>, <a href="/search/cs?searchtype=author&query=Farahani%2C+K">Keyvan Farahani</a>, <a href="/search/cs?searchtype=author&query=Ferrer%2C+L">Luciana Ferrer</a>, <a href="/search/cs?searchtype=author&query=Galdran%2C+A">Adrian Galdran</a>, <a href="/search/cs?searchtype=author&query=van+Ginneken%2C+B">Bram van Ginneken</a> , et al. (53 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.01790v4-abstract-short" style="display: inline;"> Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibilit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.01790v4-abstract-full').style.display = 'inline'; document.getElementById('2302.01790v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.01790v4-abstract-full" style="display: none;"> Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.01790v4-abstract-full').style.display = 'none'; document.getElementById('2302.01790v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Shared first authors: Annika Reinke and Minu D. Tizabi; shared senior authors: Lena Maier-Hein and Paul F. J盲ger. Published in Nature Methods. arXiv admin note: text overlap with arXiv:2206.01653</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nature methods, 1-13 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.12028">arXiv:2301.12028</a> <span> [<a href="https://arxiv.org/pdf/2301.12028">pdf</a>, <a href="https://arxiv.org/ps/2301.12028">ps</a>, <a href="https://arxiv.org/format/2301.12028">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Complexity">cs.CC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> </div> </div> <p class="title is-5 mathjax"> An Arithmetic Theory for the Poly-Time Random Functions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Melissa Antonelli</a>, <a href="/search/cs?searchtype=author&query=Lago%2C+U+D">Ugo Dal Lago</a>, <a href="/search/cs?searchtype=author&query=Davoli%2C+D">Davide Davoli</a>, <a href="/search/cs?searchtype=author&query=Oitavem%2C+I">Isabel Oitavem</a>, <a href="/search/cs?searchtype=author&query=Pistone%2C+P">Paolo Pistone</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.12028v2-abstract-short" style="display: inline;"> We introduce a new bounded theory RS^1_2 and show that the functions which are Sigma^b_1-representable in it are precisely random functions which can be computed in polynomial time. Concretely, we pass through a class of oracle functions over string, called POR, together with the theory of arithmetic RS^1_2. Then, we show that functions computed by poly-time PTMs are arithmetically characterized b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.12028v2-abstract-full').style.display = 'inline'; document.getElementById('2301.12028v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.12028v2-abstract-full" style="display: none;"> We introduce a new bounded theory RS^1_2 and show that the functions which are Sigma^b_1-representable in it are precisely random functions which can be computed in polynomial time. Concretely, we pass through a class of oracle functions over string, called POR, together with the theory of arithmetic RS^1_2. Then, we show that functions computed by poly-time PTMs are arithmetically characterized by a class of probabilistic bounded formulas. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.12028v2-abstract-full').style.display = 'none'; document.getElementById('2301.12028v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">37 pages, pre-print</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.16160">arXiv:2210.16160</a> <span> [<a href="https://arxiv.org/pdf/2210.16160">pdf</a>, <a href="https://arxiv.org/ps/2210.16160">ps</a>, <a href="https://arxiv.org/format/2210.16160">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> </div> </div> <p class="title is-5 mathjax"> Some Remarks on Counting Propositional Logic </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Melissa Antonelli</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.16160v2-abstract-short" style="display: inline;"> Counting propositional logic was recently introduced in relation to randomized computation and shown able to logically characterize the full counting hierarchy. In this paper we aim to clarify the intuitive meaning and expressive power of its univariate fragment. On the one hand, we provide an effective procedure to measure the probability of counting formulas. On the other, we make the connection… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.16160v2-abstract-full').style.display = 'inline'; document.getElementById('2210.16160v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.16160v2-abstract-full" style="display: none;"> Counting propositional logic was recently introduced in relation to randomized computation and shown able to logically characterize the full counting hierarchy. In this paper we aim to clarify the intuitive meaning and expressive power of its univariate fragment. On the one hand, we provide an effective procedure to measure the probability of counting formulas. On the other, we make the connection between this logic and stochastic experiments explicit, proving that the counting language can simulate any (and only) event associated with dyadic distributions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.16160v2-abstract-full').style.display = 'none'; document.getElementById('2210.16160v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">joint work with Ugo Dal Lago and Paolo Pistone</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.01653">arXiv:2206.01653</a> <span> [<a href="https://arxiv.org/pdf/2206.01653">pdf</a>, <a href="https://arxiv.org/format/2206.01653">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41592-023-02151-z">10.1038/s41592-023-02151-z <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Metrics reloaded: Recommendations for image analysis validation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Maier-Hein%2C+L">Lena Maier-Hein</a>, <a href="/search/cs?searchtype=author&query=Reinke%2C+A">Annika Reinke</a>, <a href="/search/cs?searchtype=author&query=Godau%2C+P">Patrick Godau</a>, <a href="/search/cs?searchtype=author&query=Tizabi%2C+M+D">Minu D. Tizabi</a>, <a href="/search/cs?searchtype=author&query=Buettner%2C+F">Florian Buettner</a>, <a href="/search/cs?searchtype=author&query=Christodoulou%2C+E">Evangelia Christodoulou</a>, <a href="/search/cs?searchtype=author&query=Glocker%2C+B">Ben Glocker</a>, <a href="/search/cs?searchtype=author&query=Isensee%2C+F">Fabian Isensee</a>, <a href="/search/cs?searchtype=author&query=Kleesiek%2C+J">Jens Kleesiek</a>, <a href="/search/cs?searchtype=author&query=Kozubek%2C+M">Michal Kozubek</a>, <a href="/search/cs?searchtype=author&query=Reyes%2C+M">Mauricio Reyes</a>, <a href="/search/cs?searchtype=author&query=Riegler%2C+M+A">Michael A. Riegler</a>, <a href="/search/cs?searchtype=author&query=Wiesenfarth%2C+M">Manuel Wiesenfarth</a>, <a href="/search/cs?searchtype=author&query=Kavur%2C+A+E">A. Emre Kavur</a>, <a href="/search/cs?searchtype=author&query=Sudre%2C+C+H">Carole H. Sudre</a>, <a href="/search/cs?searchtype=author&query=Baumgartner%2C+M">Michael Baumgartner</a>, <a href="/search/cs?searchtype=author&query=Eisenmann%2C+M">Matthias Eisenmann</a>, <a href="/search/cs?searchtype=author&query=Heckmann-N%C3%B6tzel%2C+D">Doreen Heckmann-N枚tzel</a>, <a href="/search/cs?searchtype=author&query=R%C3%A4dsch%2C+T">Tim R盲dsch</a>, <a href="/search/cs?searchtype=author&query=Acion%2C+L">Laura Acion</a>, <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Michela Antonelli</a>, <a href="/search/cs?searchtype=author&query=Arbel%2C+T">Tal Arbel</a>, <a href="/search/cs?searchtype=author&query=Bakas%2C+S">Spyridon Bakas</a>, <a href="/search/cs?searchtype=author&query=Benis%2C+A">Arriel Benis</a>, <a href="/search/cs?searchtype=author&query=Blaschko%2C+M">Matthew Blaschko</a> , et al. (49 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2206.01653v8-abstract-short" style="display: inline;"> Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, our large international ex… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.01653v8-abstract-full').style.display = 'inline'; document.getElementById('2206.01653v8-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.01653v8-abstract-full" style="display: none;"> Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, our large international expert consortium created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool, which also provides a point of access to explore weaknesses, strengths and specific recommendations for the most common validation metrics. The broad applicability of our framework across domains is demonstrated by an instantiation for various biological and medical image analysis use cases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.01653v8-abstract-full').style.display = 'none'; document.getElementById('2206.01653v8-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Shared first authors: Lena Maier-Hein, Annika Reinke. arXiv admin note: substantial text overlap with arXiv:2104.05642 Published in Nature Methods</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nature methods, 1-18 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.11265">arXiv:2203.11265</a> <span> [<a href="https://arxiv.org/pdf/2203.11265">pdf</a>, <a href="https://arxiv.org/ps/2203.11265">ps</a>, <a href="https://arxiv.org/format/2203.11265">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Logic">math.LO</span> </div> </div> <p class="title is-5 mathjax"> Curry and Howard Meet Borel </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Melissa Antonelli</a>, <a href="/search/cs?searchtype=author&query=Lago%2C+U+D">Ugo Dal Lago</a>, <a href="/search/cs?searchtype=author&query=Pistone%2C+P">Paolo Pistone</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.11265v1-abstract-short" style="display: inline;"> We show that an intuitionistic version of counting propositional logic corresponds, in the sense of Curry and Howard, to an expressive type system for the probabilistic event lambda-calculus, a vehicle calculus in which both call-by-name and call-by-value evaluation of discrete randomized functional programs can be simulated. Remarkably, proofs (respectively, types) do not only guarantee that vali… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.11265v1-abstract-full').style.display = 'inline'; document.getElementById('2203.11265v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.11265v1-abstract-full" style="display: none;"> We show that an intuitionistic version of counting propositional logic corresponds, in the sense of Curry and Howard, to an expressive type system for the probabilistic event lambda-calculus, a vehicle calculus in which both call-by-name and call-by-value evaluation of discrete randomized functional programs can be simulated. Remarkably, proofs (respectively, types) do not only guarantee that validity (respectively, termination) holds, but also reveal the underlying probability. We finally show that by endowing the type system with an intersection operator, one obtains a system precisely capturing the probabilistic behavior of lambda-terms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.11265v1-abstract-full').style.display = 'none'; document.getElementById('2203.11265v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> F.4.1; F.3.2; D.3.1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.15228">arXiv:2109.15228</a> <span> [<a href="https://arxiv.org/pdf/2109.15228">pdf</a>, <a href="https://arxiv.org/format/2109.15228">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Adapting Bandit Algorithms for Settings with Sequentially Available Arms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gabrielli%2C+M">Marco Gabrielli</a>, <a href="/search/cs?searchtype=author&query=Trov%C3%B2%2C+F">Francesco Trov貌</a>, <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Manuela Antonelli</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2109.15228v1-abstract-short" style="display: inline;"> Although the classical version of the Multi-Armed Bandits (MAB) framework has been applied successfully to several practical problems, in many real-world applications, the possible actions are not presented to the learner simultaneously, such as in the Internet campaign management and environmental monitoring settings. Instead, in such applications, a set of options is presented sequentially to th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.15228v1-abstract-full').style.display = 'inline'; document.getElementById('2109.15228v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.15228v1-abstract-full" style="display: none;"> Although the classical version of the Multi-Armed Bandits (MAB) framework has been applied successfully to several practical problems, in many real-world applications, the possible actions are not presented to the learner simultaneously, such as in the Internet campaign management and environmental monitoring settings. Instead, in such applications, a set of options is presented sequentially to the learner within a time span, and this process is repeated throughout a time horizon. At each time, the learner is asked whether to select the proposed option or not. We define this scenario as the Sequential Pull/No-pull Bandit setting, and we propose a meta-algorithm, namely Sequential Pull/No-pull for MAB (Seq), to adapt any classical MAB policy to better suit this setting for both the regret minimization and best-arm identification problems. By allowing the selection of multiple arms within a round, the proposed meta-algorithm gathers more information, especially in the first rounds, characterized by a high uncertainty in the arms estimate value. At the same time, the adapted algorithms provide the same theoretical guarantees as the classical policy employed. The Seq meta-algorithm was extensively tested and compared with classical MAB policies on synthetic and real-world datasets from advertising and environmental monitoring applications, highlighting its good empirical performances. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.15228v1-abstract-full').style.display = 'none'; document.getElementById('2109.15228v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.05735">arXiv:2106.05735</a> <span> [<a href="https://arxiv.org/pdf/2106.05735">pdf</a>, <a href="https://arxiv.org/format/2106.05735">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41467-022-30695-9">10.1038/s41467-022-30695-9 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The Medical Segmentation Decathlon </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Michela Antonelli</a>, <a href="/search/cs?searchtype=author&query=Reinke%2C+A">Annika Reinke</a>, <a href="/search/cs?searchtype=author&query=Bakas%2C+S">Spyridon Bakas</a>, <a href="/search/cs?searchtype=author&query=Farahani%2C+K">Keyvan Farahani</a>, <a href="/search/cs?searchtype=author&query=AnnetteKopp-Schneider"> AnnetteKopp-Schneider</a>, <a href="/search/cs?searchtype=author&query=Landman%2C+B+A">Bennett A. Landman</a>, <a href="/search/cs?searchtype=author&query=Litjens%2C+G">Geert Litjens</a>, <a href="/search/cs?searchtype=author&query=Menze%2C+B">Bjoern Menze</a>, <a href="/search/cs?searchtype=author&query=Ronneberger%2C+O">Olaf Ronneberger</a>, <a href="/search/cs?searchtype=author&query=Summers%2C+R+M">Ronald M. Summers</a>, <a href="/search/cs?searchtype=author&query=van+Ginneken%2C+B">Bram van Ginneken</a>, <a href="/search/cs?searchtype=author&query=Bilello%2C+M">Michel Bilello</a>, <a href="/search/cs?searchtype=author&query=Bilic%2C+P">Patrick Bilic</a>, <a href="/search/cs?searchtype=author&query=Christ%2C+P+F">Patrick F. Christ</a>, <a href="/search/cs?searchtype=author&query=Do%2C+R+K+G">Richard K. G. Do</a>, <a href="/search/cs?searchtype=author&query=Gollub%2C+M+J">Marc J. Gollub</a>, <a href="/search/cs?searchtype=author&query=Heckers%2C+S+H">Stephan H. Heckers</a>, <a href="/search/cs?searchtype=author&query=Huisman%2C+H">Henkjan Huisman</a>, <a href="/search/cs?searchtype=author&query=Jarnagin%2C+W+R">William R. Jarnagin</a>, <a href="/search/cs?searchtype=author&query=McHugo%2C+M+K">Maureen K. McHugo</a>, <a href="/search/cs?searchtype=author&query=Napel%2C+S">Sandy Napel</a>, <a href="/search/cs?searchtype=author&query=Pernicka%2C+J+S+G">Jennifer S. Goli Pernicka</a>, <a href="/search/cs?searchtype=author&query=Rhode%2C+K">Kawal Rhode</a>, <a href="/search/cs?searchtype=author&query=Tobon-Gomez%2C+C">Catalina Tobon-Gomez</a>, <a href="/search/cs?searchtype=author&query=Vorontsov%2C+E">Eugene Vorontsov</a> , et al. (34 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2106.05735v1-abstract-short" style="display: inline;"> International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical pro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.05735v1-abstract-full').style.display = 'inline'; document.getElementById('2106.05735v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.05735v1-abstract-full" style="display: none;"> International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.05735v1-abstract-full').style.display = 'none'; document.getElementById('2106.05735v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.12124">arXiv:2104.12124</a> <span> [<a href="https://arxiv.org/pdf/2104.12124">pdf</a>, <a href="https://arxiv.org/ps/2104.12124">ps</a>, <a href="https://arxiv.org/format/2104.12124">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Logic">math.LO</span> </div> </div> <p class="title is-5 mathjax"> On Measure Quantifiers in First-Order Arithmetic (Long Version) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Melissa Antonelli</a>, <a href="/search/cs?searchtype=author&query=Lago%2C+U+D">Ugo Dal Lago</a>, <a href="/search/cs?searchtype=author&query=Pistone%2C+P">Paolo Pistone</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2104.12124v1-abstract-short" style="display: inline;"> We study the logic obtained by endowing the language of first-order arithmetic with second-order measure quantifiers. This new kind of quantification allows us to express that the argument formula is true in a certain portion of all possible interpretations of the quantified variable. We show that first-order arithmetic with measure quantifiers is capable of formalizing simple results from probabi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.12124v1-abstract-full').style.display = 'inline'; document.getElementById('2104.12124v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.12124v1-abstract-full" style="display: none;"> We study the logic obtained by endowing the language of first-order arithmetic with second-order measure quantifiers. This new kind of quantification allows us to express that the argument formula is true in a certain portion of all possible interpretations of the quantified variable. We show that first-order arithmetic with measure quantifiers is capable of formalizing simple results from probability theory and, most importantly, of representing every recursive random function. Moreover, we introduce a realizability interpretation of this logic in which programs have access to an oracle from the Cantor space. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.12124v1-abstract-full').style.display = 'none'; document.getElementById('2104.12124v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> F.1.1; F.1.2; F.4.1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.05642">arXiv:2104.05642</a> <span> [<a href="https://arxiv.org/pdf/2104.05642">pdf</a>, <a href="https://arxiv.org/format/2104.05642">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Common Limitations of Image Processing Metrics: A Picture Story </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Reinke%2C+A">Annika Reinke</a>, <a href="/search/cs?searchtype=author&query=Tizabi%2C+M+D">Minu D. Tizabi</a>, <a href="/search/cs?searchtype=author&query=Sudre%2C+C+H">Carole H. Sudre</a>, <a href="/search/cs?searchtype=author&query=Eisenmann%2C+M">Matthias Eisenmann</a>, <a href="/search/cs?searchtype=author&query=R%C3%A4dsch%2C+T">Tim R盲dsch</a>, <a href="/search/cs?searchtype=author&query=Baumgartner%2C+M">Michael Baumgartner</a>, <a href="/search/cs?searchtype=author&query=Acion%2C+L">Laura Acion</a>, <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Michela Antonelli</a>, <a href="/search/cs?searchtype=author&query=Arbel%2C+T">Tal Arbel</a>, <a href="/search/cs?searchtype=author&query=Bakas%2C+S">Spyridon Bakas</a>, <a href="/search/cs?searchtype=author&query=Bankhead%2C+P">Peter Bankhead</a>, <a href="/search/cs?searchtype=author&query=Benis%2C+A">Arriel Benis</a>, <a href="/search/cs?searchtype=author&query=Blaschko%2C+M">Matthew Blaschko</a>, <a href="/search/cs?searchtype=author&query=Buettner%2C+F">Florian Buettner</a>, <a href="/search/cs?searchtype=author&query=Cardoso%2C+M+J">M. Jorge Cardoso</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J">Jianxu Chen</a>, <a href="/search/cs?searchtype=author&query=Cheplygina%2C+V">Veronika Cheplygina</a>, <a href="/search/cs?searchtype=author&query=Christodoulou%2C+E">Evangelia Christodoulou</a>, <a href="/search/cs?searchtype=author&query=Cimini%2C+B">Beth Cimini</a>, <a href="/search/cs?searchtype=author&query=Collins%2C+G+S">Gary S. Collins</a>, <a href="/search/cs?searchtype=author&query=Engelhardt%2C+S">Sandy Engelhardt</a>, <a href="/search/cs?searchtype=author&query=Farahani%2C+K">Keyvan Farahani</a>, <a href="/search/cs?searchtype=author&query=Ferrer%2C+L">Luciana Ferrer</a>, <a href="/search/cs?searchtype=author&query=Galdran%2C+A">Adrian Galdran</a>, <a href="/search/cs?searchtype=author&query=van+Ginneken%2C+B">Bram van Ginneken</a> , et al. (68 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2104.05642v8-abstract-short" style="display: inline;"> While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using spe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.05642v8-abstract-full').style.display = 'inline'; document.getElementById('2104.05642v8-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.05642v8-abstract-full" style="display: none;"> While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.05642v8-abstract-full').style.display = 'none'; document.getElementById('2104.05642v8-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Shared first authors: Annika Reinke and Minu D. Tizabi. This is a dynamic paper on limitations of commonly used metrics. It discusses metrics for image-level classification, semantic and instance segmentation, and object detection. For missing use cases, comments or questions, please contact a.reinke@dkfz.de. Substantial contributions to this document will be acknowledged with a co-authorship</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.12862">arXiv:2103.12862</a> <span> [<a href="https://arxiv.org/pdf/2103.12862">pdf</a>, <a href="https://arxiv.org/ps/2103.12862">ps</a>, <a href="https://arxiv.org/format/2103.12862">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Complexity">cs.CC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Programming Languages">cs.PL</span> </div> </div> <p class="title is-5 mathjax"> On Counting Propositional Logic </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Melissa Antonelli</a>, <a href="/search/cs?searchtype=author&query=Lago%2C+U+D">Ugo Dal Lago</a>, <a href="/search/cs?searchtype=author&query=Pistone%2C+P">Paolo Pistone</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2103.12862v2-abstract-short" style="display: inline;"> We study counting propositional logic as an extension of propositional logic with counting quantifiers. We prove that the complexity of the underlying decision problem perfectly matches the appropriate level of Wagner's counting hierarchy, but also that the resulting logic admits a satisfactory proof-theoretical treatment. From the latter, a type system for a probabilistic lambda-calculus is deriv… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.12862v2-abstract-full').style.display = 'inline'; document.getElementById('2103.12862v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.12862v2-abstract-full" style="display: none;"> We study counting propositional logic as an extension of propositional logic with counting quantifiers. We prove that the complexity of the underlying decision problem perfectly matches the appropriate level of Wagner's counting hierarchy, but also that the resulting logic admits a satisfactory proof-theoretical treatment. From the latter, a type system for a probabilistic lambda-calculus is derived in the spirit of the Curry-Howard correspondence, showing the potential of counting propositional logic as a useful tool in several fields of theoretical computer science. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.12862v2-abstract-full').style.display = 'none'; document.getElementById('2103.12862v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> F.4.1; F.1.3; D.3.1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.00867">arXiv:2011.00867</a> <span> [<a href="https://arxiv.org/pdf/2011.00867">pdf</a>, <a href="https://arxiv.org/format/2011.00867">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Murray%2C+B">Benjamin Murray</a>, <a href="/search/cs?searchtype=author&query=Kerfoot%2C+E">Eric Kerfoot</a>, <a href="/search/cs?searchtype=author&query=Graham%2C+M+S">Mark S. Graham</a>, <a href="/search/cs?searchtype=author&query=Sudre%2C+C+H">Carole H. Sudre</a>, <a href="/search/cs?searchtype=author&query=Molteni%2C+E">Erika Molteni</a>, <a href="/search/cs?searchtype=author&query=Canas%2C+L+S">Liane S. Canas</a>, <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Michela Antonelli</a>, <a href="/search/cs?searchtype=author&query=Klaser%2C+K">Kerstin Klaser</a>, <a href="/search/cs?searchtype=author&query=Visconti%2C+A">Alessia Visconti</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+A+T">Andrew T. Chan</a>, <a href="/search/cs?searchtype=author&query=Franks%2C+P+W">Paul W. Franks</a>, <a href="/search/cs?searchtype=author&query=Davies%2C+R">Richard Davies</a>, <a href="/search/cs?searchtype=author&query=Wolf%2C+J">Jonathan Wolf</a>, <a href="/search/cs?searchtype=author&query=Spector%2C+T">Tim Spector</a>, <a href="/search/cs?searchtype=author&query=Steves%2C+C+J">Claire J. Steves</a>, <a href="/search/cs?searchtype=author&query=Modat%2C+M">Marc Modat</a>, <a href="/search/cs?searchtype=author&query=Ourselin%2C+S">Sebastien Ourselin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2011.00867v2-abstract-short" style="display: inline;"> The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. Over 4.7 million participants and 189 million unique assessments have been logged since its introduction in March 2020. The success of the Covid Symptom Study creates technical challenges around effective data curation for two reasons. Firstly, the scal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.00867v2-abstract-full').style.display = 'inline'; document.getElementById('2011.00867v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.00867v2-abstract-full" style="display: none;"> The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. Over 4.7 million participants and 189 million unique assessments have been logged since its introduction in March 2020. The success of the Covid Symptom Study creates technical challenges around effective data curation for two reasons. Firstly, the scale of the dataset means that it can no longer be easily processed using standard software on commodity hardware. Secondly, the size of the research group means that replicability and consistency of key analytics used across multiple publications becomes an issue. We present ExeTera, an open source data curation software designed to address scalability challenges and to enable reproducible research across an international research group for datasets such as the Covid Symptom Study dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.00867v2-abstract-full').style.display = 'none'; document.getElementById('2011.00867v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> D.m; E.2; H.3.3; I.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1902.09063">arXiv:1902.09063</a> <span> [<a href="https://arxiv.org/pdf/1902.09063">pdf</a>, <a href="https://arxiv.org/format/1902.09063">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> A large annotated medical image dataset for the development and evaluation of segmentation algorithms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Simpson%2C+A+L">Amber L. Simpson</a>, <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Michela Antonelli</a>, <a href="/search/cs?searchtype=author&query=Bakas%2C+S">Spyridon Bakas</a>, <a href="/search/cs?searchtype=author&query=Bilello%2C+M">Michel Bilello</a>, <a href="/search/cs?searchtype=author&query=Farahani%2C+K">Keyvan Farahani</a>, <a href="/search/cs?searchtype=author&query=van+Ginneken%2C+B">Bram van Ginneken</a>, <a href="/search/cs?searchtype=author&query=Kopp-Schneider%2C+A">Annette Kopp-Schneider</a>, <a href="/search/cs?searchtype=author&query=Landman%2C+B+A">Bennett A. Landman</a>, <a href="/search/cs?searchtype=author&query=Litjens%2C+G">Geert Litjens</a>, <a href="/search/cs?searchtype=author&query=Menze%2C+B">Bjoern Menze</a>, <a href="/search/cs?searchtype=author&query=Ronneberger%2C+O">Olaf Ronneberger</a>, <a href="/search/cs?searchtype=author&query=Summers%2C+R+M">Ronald M. Summers</a>, <a href="/search/cs?searchtype=author&query=Bilic%2C+P">Patrick Bilic</a>, <a href="/search/cs?searchtype=author&query=Christ%2C+P+F">Patrick F. Christ</a>, <a href="/search/cs?searchtype=author&query=Do%2C+R+K+G">Richard K. G. Do</a>, <a href="/search/cs?searchtype=author&query=Gollub%2C+M">Marc Gollub</a>, <a href="/search/cs?searchtype=author&query=Golia-Pernicka%2C+J">Jennifer Golia-Pernicka</a>, <a href="/search/cs?searchtype=author&query=Heckers%2C+S+H">Stephan H. Heckers</a>, <a href="/search/cs?searchtype=author&query=Jarnagin%2C+W+R">William R. Jarnagin</a>, <a href="/search/cs?searchtype=author&query=McHugo%2C+M+K">Maureen K. McHugo</a>, <a href="/search/cs?searchtype=author&query=Napel%2C+S">Sandy Napel</a>, <a href="/search/cs?searchtype=author&query=Vorontsov%2C+E">Eugene Vorontsov</a>, <a href="/search/cs?searchtype=author&query=Maier-Hein%2C+L">Lena Maier-Hein</a>, <a href="/search/cs?searchtype=author&query=Cardoso%2C+M+J">M. Jorge Cardoso</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1902.09063v1-abstract-short" style="display: inline;"> Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomie… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.09063v1-abstract-full').style.display = 'inline'; document.getElementById('1902.09063v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1902.09063v1-abstract-full" style="display: none;"> Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.09063v1-abstract-full').style.display = 'none'; document.getElementById('1902.09063v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1901.04056">arXiv:1901.04056</a> <span> [<a href="https://arxiv.org/pdf/1901.04056">pdf</a>, <a href="https://arxiv.org/format/1901.04056">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.media.2022.102680">10.1016/j.media.2022.102680 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The Liver Tumor Segmentation Benchmark (LiTS) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bilic%2C+P">Patrick Bilic</a>, <a href="/search/cs?searchtype=author&query=Christ%2C+P">Patrick Christ</a>, <a href="/search/cs?searchtype=author&query=Li%2C+H+B">Hongwei Bran Li</a>, <a href="/search/cs?searchtype=author&query=Vorontsov%2C+E">Eugene Vorontsov</a>, <a href="/search/cs?searchtype=author&query=Ben-Cohen%2C+A">Avi Ben-Cohen</a>, <a href="/search/cs?searchtype=author&query=Kaissis%2C+G">Georgios Kaissis</a>, <a href="/search/cs?searchtype=author&query=Szeskin%2C+A">Adi Szeskin</a>, <a href="/search/cs?searchtype=author&query=Jacobs%2C+C">Colin Jacobs</a>, <a href="/search/cs?searchtype=author&query=Mamani%2C+G+E+H">Gabriel Efrain Humpire Mamani</a>, <a href="/search/cs?searchtype=author&query=Chartrand%2C+G">Gabriel Chartrand</a>, <a href="/search/cs?searchtype=author&query=Loh%C3%B6fer%2C+F">Fabian Loh枚fer</a>, <a href="/search/cs?searchtype=author&query=Holch%2C+J+W">Julian Walter Holch</a>, <a href="/search/cs?searchtype=author&query=Sommer%2C+W">Wieland Sommer</a>, <a href="/search/cs?searchtype=author&query=Hofmann%2C+F">Felix Hofmann</a>, <a href="/search/cs?searchtype=author&query=Hostettler%2C+A">Alexandre Hostettler</a>, <a href="/search/cs?searchtype=author&query=Lev-Cohain%2C+N">Naama Lev-Cohain</a>, <a href="/search/cs?searchtype=author&query=Drozdzal%2C+M">Michal Drozdzal</a>, <a href="/search/cs?searchtype=author&query=Amitai%2C+M+M">Michal Marianne Amitai</a>, <a href="/search/cs?searchtype=author&query=Vivantik%2C+R">Refael Vivantik</a>, <a href="/search/cs?searchtype=author&query=Sosna%2C+J">Jacob Sosna</a>, <a href="/search/cs?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/cs?searchtype=author&query=Sekuboyina%2C+A">Anjany Sekuboyina</a>, <a href="/search/cs?searchtype=author&query=Navarro%2C+F">Fernando Navarro</a>, <a href="/search/cs?searchtype=author&query=Kofler%2C+F">Florian Kofler</a>, <a href="/search/cs?searchtype=author&query=Paetzold%2C+J+C">Johannes C. Paetzold</a> , et al. (84 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1901.04056v2-abstract-short" style="display: inline;"> In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.04056v2-abstract-full').style.display = 'inline'; document.getElementById('1901.04056v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1901.04056v2-abstract-full" style="display: none;"> In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in \url{http://medicaldecathlon.com/}. In addition, both data and online evaluation are accessible via \url{www.lits-challenge.com}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.04056v2-abstract-full').style.display = 'none'; document.getElementById('1901.04056v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Patrick Bilic, Patrick Christ, Hongwei Bran Li, and Eugene Vorontsov made equal contributions to this work. Published in Medical Image Analysis</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Medical Image Analysis (2022) Pg. 102680 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1610.07129">arXiv:1610.07129</a> <span> [<a href="https://arxiv.org/pdf/1610.07129">pdf</a>, <a href="https://arxiv.org/ps/1610.07129">ps</a>, <a href="https://arxiv.org/format/1610.07129">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Developing and Assessing MATLAB Exercises for Active Concept Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Song%2C+S+H">S. H. Song</a>, <a href="/search/cs?searchtype=author&query=Antonelli%2C+M">Marco Antonelli</a>, <a href="/search/cs?searchtype=author&query=Fung%2C+T">Tony Fung</a>, <a href="/search/cs?searchtype=author&query=Armstrong%2C+B+D">Brandon D. Armstrong</a>, <a href="/search/cs?searchtype=author&query=Chong%2C+A">Amy Chong</a>, <a href="/search/cs?searchtype=author&query=Lo%2C+A">Albert Lo</a>, <a href="/search/cs?searchtype=author&query=Shi%2C+B+E">Bertram E. Shi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1610.07129v1-abstract-short" style="display: inline;"> New technologies, such as MOOCs, provide innovative methods to tackle new challenges in teaching and learning, such as globalization and changing contemporary culture and to remove the limits of conventional classrooms. However, they also bring challenges in course delivery and assessment, due to factors such as less direct student-instructor interaction. These challenges are especially severe in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1610.07129v1-abstract-full').style.display = 'inline'; document.getElementById('1610.07129v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1610.07129v1-abstract-full" style="display: none;"> New technologies, such as MOOCs, provide innovative methods to tackle new challenges in teaching and learning, such as globalization and changing contemporary culture and to remove the limits of conventional classrooms. However, they also bring challenges in course delivery and assessment, due to factors such as less direct student-instructor interaction. These challenges are especially severe in engineering education, which relies heavily on experiential learning, such as computer simulations and laboratory exercises, to assist students in understanding concepts. As a result, effective design of experiential learning components is extremely critical for engineering MOOCs. In this paper, we will share our experience gained through developing and offering a MOOC on communication systems, with special focus on the development and assessment of MATLAB exercises for active concept learning. Our approach introduced students to concepts using learning components commonly provided by many MOOC platforms (e.g., online lectures and quizzes), and augmented the student experience with MATLAB based computer simulations and exercises to enable more concrete and detailed understanding of the material. We describe here a systematic approach to MATLAB problem design and assessment, based on our experience with the MATLAB server provided by MathWorks and integrated with the edX MOOC platform. We discuss the effectiveness of the instructional methods as evaluated through students' learning performance. We analyze the impact of the course design tools from both the instructor and the student perspective. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1610.07129v1-abstract-full').style.display = 'none'; document.getElementById('1610.07129v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 October, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to IEEE Transactions on Education</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> 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