Titlebook: Ethics and Fairness in Medical Imaging; Second International Esther Puyol-Antón,Ghada Zamzmi,Roy Eagleson Conference proceedings 2025 The E

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/f/image/320731.jpg

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978-3-031-72786-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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https://doi.org/10.1007/978-1-4615-0051-3f skin lesion classification using ResNet-based CNNs, focusing on patient sex variations in training data and three different learning strategies. We present a linear programming method for generating datasets with varying patient sex and class labels, taking into account the correlations between th

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https://doi.org/10.1007/978-3-658-37093-0or, affects the presentation of disease in medical images, and hence the performance of AI algorithms. Existing fairness criteria such as equalized odds do not capture this effect, as is illustrated by an example. Additionally, a new metric is proposed based on the information theoretic notion of ad

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https://doi.org/10.1007/978-3-319-60777-1ally changes the appearance of Computed Tomography (CT) images, which - if unknown - can significantly deteriorate the diagnostic performance of neural networks. Artificial Intelligence (AI) can help to detect IV contrast, reducing the need for labour-intensive and error-prone manual labeling. Howev

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