DMDSC adapts simplex-classifier margins dynamically according to label frequency to tighten clustering on rare medical classes and improve open-set rejection on imbalanced imaging datasets.
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,
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Post-hoc normalizing flows for OOD detection in medical imaging achieve 84.61% AUROC on MedOOD and 93.8% on MedMNIST, outperforming ViM, MDS, and ReAct.
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DMDSC: A Dynamic-Margin Deep Simplex Classifier for Open-Set Recognition on Medical Image Datasets
DMDSC adapts simplex-classifier margins dynamically according to label frequency to tighten clustering on rare medical classes and improve open-set rejection on imbalanced imaging datasets.
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Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows
Post-hoc normalizing flows for OOD detection in medical imaging achieve 84.61% AUROC on MedOOD and 93.8% on MedMNIST, outperforming ViM, MDS, and ReAct.