We develop a trustworthy medical image segmentation framework named EvidenceCap, which works like an identity cap that provides robustness, confidence, and high efficiency for medical image segmentation. EvidenceCap renders the output of the underlying network in an evidence-level manner. This not only estimates a stable and reasonable pixel ...
view more202252 · In this research, we address the question to which extent bias occurs in medical FL and how to prevent excessive bias through reward systems. We first evaluate how to measure the contributions of institutions toward predictive performance and bias in cross-silo medical FL with a Shapley value approximation method.
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view moreThe ethical framework of ethical values-ethical principles-ethical norms is used to propose corresponding ethical governance countermeasures for trustworthy medical AI from the ethical, legal, and regulatory aspects.
view more202311 · Medical image segmentation is critical for disease diagnosis and treatment assessment. However, concerns regarding the reliability of segmentation regions persist among clinicians, mainly attributed to the absence of confidence assessment, robustness, and calibration to accuracy. To address this, we introduce DEviS, an easily …
view more202251 · Reward Systems for Trustworthy Medical Federated Learning. The results indicate that reward systems, which focus on predictive performance only, can transfer model bias against patients to an institutional level, and an integrated reward system successfully incentivizes contributions toward a well-performing model with low bias.
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view more2023113 · The ethical framework of ethical values-ethical principles-ethical norms is used to propose corresponding ethical governance countermeasures for trustworthy medical AI from the ethical, legal, and regulatory aspects. Results: Medical data are primarily unstructured, lacking uniform and standardized annotation, and data quality will directly ...
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