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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/2301.08330">arXiv:2301.08330</a> <span> [<a href="https://arxiv.org/pdf/2301.08330">pdf</a>, <a href="https://arxiv.org/format/2301.08330">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"> The role of noise in denoising models for anomaly detection in medical images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kascenas%2C+A">Antanas Kascenas</a>, <a href="/search/cs?searchtype=author&query=Sanchez%2C+P">Pedro Sanchez</a>, <a href="/search/cs?searchtype=author&query=Schrempf%2C+P">Patrick Schrempf</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+C">Chaoyang Wang</a>, <a href="/search/cs?searchtype=author&query=Clackett%2C+W">William Clackett</a>, <a href="/search/cs?searchtype=author&query=Mikhael%2C+S+S">Shadia S. Mikhael</a>, <a href="/search/cs?searchtype=author&query=Voisey%2C+J+P">Jeremy P. Voisey</a>, <a href="/search/cs?searchtype=author&query=Goatman%2C+K">Keith Goatman</a>, <a href="/search/cs?searchtype=author&query=Weir%2C+A">Alexander Weir</a>, <a href="/search/cs?searchtype=author&query=Pugeault%2C+N">Nicolas Pugeault</a>, <a href="/search/cs?searchtype=author&query=Tsaftaris%2C+S+A">Sotirios A. Tsaftaris</a>, <a href="/search/cs?searchtype=author&query=O%27Neil%2C+A+Q">Alison Q. O'Neil</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="2301.08330v1-abstract-short" style="display: inline;"> Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for inst… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.08330v1-abstract-full').style.display = 'inline'; document.getElementById('2301.08330v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.08330v1-abstract-full" style="display: none;"> Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes, with similar noise parameter adjustments giving good performance for both DAEs and diffusion models. Visual inspection of the reconstructions suggests that the training noise influences the trade-off between the extent of the detail that is reconstructed and the extent of erasure of anomalies, both of which contribute to better anomaly detection performance. We validate our findings on two real-world datasets (tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain CT), showing good detection on diverse anomaly appearances. Overall, we find that a DAE trained with coarse noise is a fast and simple method that gives state-of-the-art accuracy. Diffusion models applied to anomaly detection are as yet in their infancy and provide a promising avenue for further research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.08330v1-abstract-full').style.display = 'none'; document.getElementById('2301.08330v1-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> 19 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">Submitted to Medical Image Analysis special issue for MIDL 2022</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T99; 92C55; 68U10 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.16152">arXiv:2007.16152</a> <span> [<a href="https://arxiv.org/pdf/2007.16152">pdf</a>, <a href="https://arxiv.org/format/2007.16152">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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"> Paying Per-label Attention for Multi-label Extraction from Radiology Reports </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Schrempf%2C+P">Patrick Schrempf</a>, <a href="/search/cs?searchtype=author&query=Watson%2C+H">Hannah Watson</a>, <a href="/search/cs?searchtype=author&query=Mikhael%2C+S">Shadia Mikhael</a>, <a href="/search/cs?searchtype=author&query=Pajak%2C+M">Maciej Pajak</a>, <a href="/search/cs?searchtype=author&query=Falis%2C+M">Mat煤拧 Falis</a>, <a href="/search/cs?searchtype=author&query=Lisowska%2C+A">Aneta Lisowska</a>, <a href="/search/cs?searchtype=author&query=Muir%2C+K+W">Keith W. Muir</a>, <a href="/search/cs?searchtype=author&query=Harris-Birtill%2C+D">David Harris-Birtill</a>, <a href="/search/cs?searchtype=author&query=O%27Neil%2C+A+Q">Alison Q. O'Neil</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="2007.16152v3-abstract-short" style="display: inline;"> Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain. Images are often accompanied by free-text radiology reports which are a rich source of information. In this paper, we tackle the automated extraction of structured labels from head CT reports for imaging of suspected stroke patients, using deep learning. Firstly… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.16152v3-abstract-full').style.display = 'inline'; document.getElementById('2007.16152v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.16152v3-abstract-full" style="display: none;"> Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain. Images are often accompanied by free-text radiology reports which are a rich source of information. In this paper, we tackle the automated extraction of structured labels from head CT reports for imaging of suspected stroke patients, using deep learning. Firstly, we propose a set of 31 labels which correspond to radiographic findings (e.g. hyperdensity) and clinical impressions (e.g. haemorrhage) related to neurological abnormalities. Secondly, inspired by previous work, we extend existing state-of-the-art neural network models with a label-dependent attention mechanism. Using this mechanism and simple synthetic data augmentation, we are able to robustly extract many labels with a single model, classified according to the radiologist's reporting (positive, uncertain, negative). This approach can be used in further research to effectively extract many labels from medical text. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.16152v3-abstract-full').style.display = 'none'; document.getElementById('2007.16152v3-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> 7 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </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 to MICCAI 2020 LABELS workshop</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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