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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/2408.02462">arXiv:2408.02462</a> <span> [<a href="https://arxiv.org/pdf/2408.02462">pdf</a>, <a href="https://arxiv.org/format/2408.02462">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="Artificial Intelligence">cs.AI</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"> An investigation into the causes of race bias in AI-based cine CMR segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lee%2C+T">Tiarna Lee</a>, <a href="/search/eess?searchtype=author&query=Puyol-Anton%2C+E">Esther Puyol-Anton</a>, <a href="/search/eess?searchtype=author&query=Ruijsink%2C+B">Bram Ruijsink</a>, <a href="/search/eess?searchtype=author&query=Roujol%2C+S">Sebastien Roujol</a>, <a href="/search/eess?searchtype=author&query=Barfoot%2C+T">Theodore Barfoot</a>, <a href="/search/eess?searchtype=author&query=Ogbomo-Harmitt%2C+S">Shaheim Ogbomo-Harmitt</a>, <a href="/search/eess?searchtype=author&query=Shi%2C+M">Miaojing Shi</a>, <a href="/search/eess?searchtype=author&query=King%2C+A+P">Andrew P. King</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="2408.02462v1-abstract-short" style="display: inline;"> Artificial intelligence (AI) methods are being used increasingly for the automated segmentation of cine cardiac magnetic resonance (CMR) imaging. However, these methods have been shown to be subject to race bias, i.e. they exhibit different levels of performance for different races depending on the (im)balance of the data used to train the AI model. In this paper we investigate the source of this… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02462v1-abstract-full').style.display = 'inline'; document.getElementById('2408.02462v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.02462v1-abstract-full" style="display: none;"> Artificial intelligence (AI) methods are being used increasingly for the automated segmentation of cine cardiac magnetic resonance (CMR) imaging. However, these methods have been shown to be subject to race bias, i.e. they exhibit different levels of performance for different races depending on the (im)balance of the data used to train the AI model. In this paper we investigate the source of this bias, seeking to understand its root cause(s) so that it can be effectively mitigated. We perform a series of classification and segmentation experiments on short-axis cine CMR images acquired from Black and White subjects from the UK Biobank and apply AI interpretability methods to understand the results. In the classification experiments, we found that race can be predicted with high accuracy from the images alone, but less accurately from ground truth segmentations, suggesting that the distributional shift between races, which is often the cause of AI bias, is mostly image-based rather than segmentation-based. The interpretability methods showed that most attention in the classification models was focused on non-heart regions, such as subcutaneous fat. Cropping the images tightly around the heart reduced classification accuracy to around chance level. Similarly, race can be predicted from the latent representations of a biased segmentation model, suggesting that race information is encoded in the model. Cropping images tightly around the heart reduced but did not eliminate segmentation bias. We also investigate the influence of possible confounders on the bias observed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02462v1-abstract-full').style.display = 'none'; document.getElementById('2408.02462v1-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> 5 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.03512">arXiv:2011.03512</a> <span> [<a href="https://arxiv.org/pdf/2011.03512">pdf</a>, <a href="https://arxiv.org/format/2011.03512">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Do We Need to Compensate for Motion Distortion and Doppler Effects in Spinning Radar Navigation? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Burnett%2C+K">Keenan Burnett</a>, <a href="/search/eess?searchtype=author&query=Schoellig%2C+A+P">Angela P. Schoellig</a>, <a href="/search/eess?searchtype=author&query=Barfoot%2C+T+D">Timothy D. Barfoot</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="2011.03512v4-abstract-short" style="display: inline;"> In order to tackle the challenge of unfavorable weather conditions such as rain and snow, radar is being revisited as a parallel sensing modality to vision and lidar. Recent works have made tremendous progress in applying spinning radar to odometry and place recognition. However, these works have so far ignored the impact of motion distortion and Doppler effects on spinning-radar-based navigation,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.03512v4-abstract-full').style.display = 'inline'; document.getElementById('2011.03512v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.03512v4-abstract-full" style="display: none;"> In order to tackle the challenge of unfavorable weather conditions such as rain and snow, radar is being revisited as a parallel sensing modality to vision and lidar. Recent works have made tremendous progress in applying spinning radar to odometry and place recognition. However, these works have so far ignored the impact of motion distortion and Doppler effects on spinning-radar-based navigation, which may be significant in the self-driving car domain where speeds can be high. In this work, we demonstrate the effect of these distortions on radar odometry using the Oxford Radar RobotCar Dataset and metric localization using our own data-taking platform. We revisit a lightweight estimator that can recover the motion between a pair of radar scans while accounting for both effects. Our conclusion is that both motion distortion and the Doppler effect are significant in different aspects of spinning radar navigation, with the former more prominent than the latter. Code for this project can be found at: https://github.com/keenan-burnett/yeti_radar_odometry <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.03512v4-abstract-full').style.display = 'none'; document.getElementById('2011.03512v4-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> 13 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 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">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication in ICRA/RA-L 2021. 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