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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Visual-Acuity

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Uncertainty-Aware Bayesian Deep Learning with Noisy Training Labels for Epileptic Seizure Detectionground truth” information about the target phenomena. In actuality, the labels, often derived from human annotations, are noisy/unreliable. This . poses significant challenges for modalities such as electroencephalography (EEG), in which “ground truth” is difficult to ascertain without invasive expe

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Diagnose with Uncertainty Awareness: Diagnostic Uncertainty Encoding Framework for Radiology Report ance the efficiency of radiologist decision-making. For clinical accuracy, most existing approaches focus on achieving accurate predictions of the existence of abnormalities, despite the inherent uncertainty impacting the reliability of the generated report, which is often clarified by radiologists

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Making Deep Learning Models Clinically Useful - Improving Diagnostic Confidence in Inherited Retinale methods lack transparency and interpretability of point predictions without assessing the quality of their outputs. Knowing how much confidence there is in a prediction is essential for gaining clinicians’ trust in the technology and its use in medical decision-making. In this paper, we explore th