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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/2407.18207">arXiv:2407.18207</a> <span> [<a href="https://arxiv.org/pdf/2407.18207">pdf</a>, <a href="https://arxiv.org/format/2407.18207">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Geometry Fidelity for Spherical Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Christensen%2C+A">Anders Christensen</a>, <a href="/search/cs?searchtype=author&query=Mojab%2C+N">Nooshin Mojab</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+K">Khushman Patel</a>, <a href="/search/cs?searchtype=author&query=Ahuja%2C+K">Karan Ahuja</a>, <a href="/search/cs?searchtype=author&query=Akata%2C+Z">Zeynep Akata</a>, <a href="/search/cs?searchtype=author&query=Winther%2C+O">Ole Winther</a>, <a href="/search/cs?searchtype=author&query=Gonzalez-Franco%2C+M">Mar Gonzalez-Franco</a>, <a href="/search/cs?searchtype=author&query=Colaco%2C+A">Andrea Colaco</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="2407.18207v1-abstract-short" style="display: inline;"> Spherical or omni-directional images offer an immersive visual format appealing to a wide range of computer vision applications. However, geometric properties of spherical images pose a major challenge for models and metrics designed for ordinary 2D images. Here, we show that direct application of Fr茅chet Inception Distance (FID) is insufficient for quantifying geometric fidelity in spherical imag… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18207v1-abstract-full').style.display = 'inline'; document.getElementById('2407.18207v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.18207v1-abstract-full" style="display: none;"> Spherical or omni-directional images offer an immersive visual format appealing to a wide range of computer vision applications. However, geometric properties of spherical images pose a major challenge for models and metrics designed for ordinary 2D images. Here, we show that direct application of Fr茅chet Inception Distance (FID) is insufficient for quantifying geometric fidelity in spherical images. We introduce two quantitative metrics accounting for geometric constraints, namely Omnidirectional FID (OmniFID) and Discontinuity Score (DS). OmniFID is an extension of FID tailored to additionally capture field-of-view requirements of the spherical format by leveraging cubemap projections. DS is a kernel-based seam alignment score of continuity across borders of 2D representations of spherical images. In experiments, OmniFID and DS quantify geometry fidelity issues that are undetected by FID. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18207v1-abstract-full').style.display = 'none'; document.getElementById('2407.18207v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">Accepted at ECCV 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.00042">arXiv:2111.00042</a> <span> [<a href="https://arxiv.org/pdf/2111.00042">pdf</a>, <a href="https://arxiv.org/format/2111.00042">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> <p class="title is-5 mathjax"> CvS: Classification via Segmentation For Small Datasets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mojab%2C+N">Nooshin Mojab</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+P+S">Philip S. Yu</a>, <a href="/search/cs?searchtype=author&query=Hallak%2C+J+A">Joelle A. Hallak</a>, <a href="/search/cs?searchtype=author&query=Yi%2C+D">Darvin Yi</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="2111.00042v1-abstract-short" style="display: inline;"> Deep learning models have shown promising results in a wide range of computer vision applications across various domains. The success of deep learning methods relies heavily on the availability of a large amount of data. Deep neural networks are prone to overfitting when data is scarce. This problem becomes even more severe for neural network with classification head with access to only a few data… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.00042v1-abstract-full').style.display = 'inline'; document.getElementById('2111.00042v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.00042v1-abstract-full" style="display: none;"> Deep learning models have shown promising results in a wide range of computer vision applications across various domains. The success of deep learning methods relies heavily on the availability of a large amount of data. Deep neural networks are prone to overfitting when data is scarce. This problem becomes even more severe for neural network with classification head with access to only a few data points. However, acquiring large-scale datasets is very challenging, laborious, or even infeasible in some domains. Hence, developing classifiers that are able to perform well in small data regimes is crucial for applications with limited data. This paper presents CvS, a cost-effective classifier for small datasets that derives the classification labels from predicting the segmentation maps. We employ the label propagation method to achieve a fully segmented dataset with only a handful of manually segmented data. We evaluate the effectiveness of our framework on diverse problems showing that CvS is able to achieve much higher classification results compared to previous methods when given only a handful of examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.00042v1-abstract-full').style.display = 'none'; document.getElementById('2111.00042v1-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> 29 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.02609">arXiv:2104.02609</a> <span> [<a href="https://arxiv.org/pdf/2104.02609">pdf</a>, <a href="https://arxiv.org/format/2104.02609">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"> I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mojab%2C+N">Nooshin Mojab</a>, <a href="/search/cs?searchtype=author&query=Noroozi%2C+V">Vahid Noroozi</a>, <a href="/search/cs?searchtype=author&query=Aleem%2C+A">Abdullah Aleem</a>, <a href="/search/cs?searchtype=author&query=Nallabothula%2C+M+P">Manoj P. Nallabothula</a>, <a href="/search/cs?searchtype=author&query=Baker%2C+J">Joseph Baker</a>, <a href="/search/cs?searchtype=author&query=Azar%2C+D+T">Dimitri T. Azar</a>, <a href="/search/cs?searchtype=author&query=Rosenblatt%2C+M">Mark Rosenblatt</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+R+P">RV Paul Chan</a>, <a href="/search/cs?searchtype=author&query=Yi%2C+D">Darvin Yi</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+P+S">Philip S. Yu</a>, <a href="/search/cs?searchtype=author&query=Hallak%2C+J+A">Joelle A. Hallak</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="2104.02609v1-abstract-short" style="display: inline;"> Data from clinical real-world settings is characterized by variability in quality, machine-type, setting, and source. One of the primary goals of medical computer vision is to develop and validate artificial intelligence (AI) based algorithms on real-world data enabling clinical translations. However, despite the exponential growth in AI based applications in healthcare, specifically in ophthalmol… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.02609v1-abstract-full').style.display = 'inline'; document.getElementById('2104.02609v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.02609v1-abstract-full" style="display: none;"> Data from clinical real-world settings is characterized by variability in quality, machine-type, setting, and source. One of the primary goals of medical computer vision is to develop and validate artificial intelligence (AI) based algorithms on real-world data enabling clinical translations. However, despite the exponential growth in AI based applications in healthcare, specifically in ophthalmology, translations to clinical settings remain challenging. Limited access to adequate and diverse real-world data inhibits the development and validation of translatable algorithms. In this paper, we present a new multi-modal longitudinal ophthalmic imaging dataset, the Illinois Ophthalmic Database Atlas (I-ODA), with the goal of advancing state-of-the-art computer vision applications in ophthalmology, and improving upon the translatable capacity of AI based applications across different clinical settings. We present the infrastructure employed to collect, annotate, and anonymize images from multiple sources, demonstrating the complexity of real-world retrospective data and its limitations. I-ODA includes 12 imaging modalities with a total of 3,668,649 ophthalmic images of 33,876 individuals from the Department of Ophthalmology and Visual Sciences at the Illinois Eye and Ear Infirmary of the University of Illinois Chicago (UIC) over the course of 12 years. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.02609v1-abstract-full').style.display = 'none'; document.getElementById('2104.02609v1-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> 29 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.12672">arXiv:2007.12672</a> <span> [<a href="https://arxiv.org/pdf/2007.12672">pdf</a>, <a href="https://arxiv.org/format/2007.12672">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> <p class="title is-5 mathjax"> Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mojab%2C+N">Nooshin Mojab</a>, <a href="/search/cs?searchtype=author&query=Noroozi%2C+V">Vahid Noroozi</a>, <a href="/search/cs?searchtype=author&query=Yi%2C+D">Darvin Yi</a>, <a href="/search/cs?searchtype=author&query=Nallabothula%2C+M+P">Manoj Prabhakar Nallabothula</a>, <a href="/search/cs?searchtype=author&query=Aleem%2C+A">Abdullah Aleem</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+P+S">Phillip S. Yu</a>, <a href="/search/cs?searchtype=author&query=Hallak%2C+J+A">Joelle A. Hallak</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="2007.12672v1-abstract-short" style="display: inline;"> With promising results of machine learning based models in computer vision, applications on medical imaging data have been increasing exponentially. However, generalizations to complex real-world clinical data is a persistent problem. Deep learning models perform well when trained on standardized datasets from artificial settings, such as clinical trials. However, real-world data is different and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.12672v1-abstract-full').style.display = 'inline'; document.getElementById('2007.12672v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.12672v1-abstract-full" style="display: none;"> With promising results of machine learning based models in computer vision, applications on medical imaging data have been increasing exponentially. However, generalizations to complex real-world clinical data is a persistent problem. Deep learning models perform well when trained on standardized datasets from artificial settings, such as clinical trials. However, real-world data is different and translations are yielding varying results. The complexity of real-world applications in healthcare could emanate from a mixture of different data distributions across multiple device domains alongside the inevitable noise sourced from varying image resolutions, human errors, and the lack of manual gradings. In addition, healthcare applications not only suffer from the scarcity of labeled data, but also face limited access to unlabeled data due to HIPAA regulations, patient privacy, ambiguity in data ownership, and challenges in collecting data from different sources. These limitations pose additional challenges to applying deep learning algorithms in healthcare and clinical translations. In this paper, we utilize self-supervised representation learning methods, formulated effectively in transfer learning settings, to address limited data availability. Our experiments verify the importance of diverse real-world data for generalization to clinical settings. We show that by employing a self-supervised approach with transfer learning on a multi-domain real-world dataset, we can achieve 16% relative improvement on a standardized dataset over supervised baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.12672v1-abstract-full').style.display = 'none'; document.getElementById('2007.12672v1-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 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1912.13230">arXiv:1912.13230</a> <span> [<a href="https://arxiv.org/pdf/1912.13230">pdf</a>, <a href="https://arxiv.org/format/1912.13230">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> <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">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Leveraging Semi-Supervised Learning for Fairness using Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Noroozi%2C+V">Vahid Noroozi</a>, <a href="/search/cs?searchtype=author&query=Bahaadini%2C+S">Sara Bahaadini</a>, <a href="/search/cs?searchtype=author&query=Sheikhi%2C+S">Samira Sheikhi</a>, <a href="/search/cs?searchtype=author&query=Mojab%2C+N">Nooshin Mojab</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+P+S">Philip S. Yu</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="1912.13230v1-abstract-short" style="display: inline;"> There has been a growing concern about the fairness of decision-making systems based on machine learning. The shortage of labeled data has been always a challenging problem facing machine learning based systems. In such scenarios, semi-supervised learning has shown to be an effective way of exploiting unlabeled data to improve upon the performance of model. Notably, unlabeled data do not contain l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.13230v1-abstract-full').style.display = 'inline'; document.getElementById('1912.13230v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1912.13230v1-abstract-full" style="display: none;"> There has been a growing concern about the fairness of decision-making systems based on machine learning. The shortage of labeled data has been always a challenging problem facing machine learning based systems. In such scenarios, semi-supervised learning has shown to be an effective way of exploiting unlabeled data to improve upon the performance of model. Notably, unlabeled data do not contain label information which itself can be a significant source of bias in training machine learning systems. This inspired us to tackle the challenge of fairness by formulating the problem in a semi-supervised framework. In this paper, we propose a semi-supervised algorithm using neural networks benefiting from unlabeled data to not just improve the performance but also improve the fairness of the decision-making process. The proposed model, called SSFair, exploits the information in the unlabeled data to mitigate the bias in the training data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.13230v1-abstract-full').style.display = 'none'; document.getElementById('1912.13230v1-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> 31 December, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 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">6 pages, 5 figures, accepted to ICMLA 2019</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> 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