Titlebook: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging; 6th International Wo Carole H. Sudre,Raghav Mehta,William M. Wells

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Diagnose with Uncertainty Awareness: Diagnostic Uncertainty Encoding Framework for Radiology Report is integrated with a report generation model under a novel visual-language encoding mechanism. Extensive experiments on two public benchmark datasets demonstrate that DiagUE could outperform SOTA baselines in ensuring the clinical accuracy of both abnormality description and diagnostic uncertainty o

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Making Deep Learning Models Clinically Useful - Improving Diagnostic Confidence in Inherited Retinale assessed: Least Ambiguous Adaptive Prediction Sets (LAPS), Adaptative Prediction Sets (APS), and Regularized Adaptive Prediction Sets (RAPS). Our IRD classifier (Eye2Gene), in combination with the three conformal predictors, was evaluated on an internal holdout subset and datasets from four extern

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Typicality Excels Likelihood for Unsupervised Out-of-Distribution Detection in Medical Imaging imaging, where test samples are juxtaposed against the probability mass rather than the density. The obtained findings demonstrate the superiority of evaluating typicality against likelihood for finding pathological abnormalities. We achieve state-of-the-art performance on the ISIC, COVID-19, and R

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