A combined logit-adjusted loss and CVaR objective improves macro F1 and reduces gender disparity in 3D CT classification of lung cancers, COVID-19, and normal cases on a benchmark with severe class and group imbalance.
Seven-point checklist and skin lesion classification using multitask multimodal neural nets
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
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JI-ADF is a trimodal model with joint-individual learning, multimodal fusion attention, and adaptive decision fusion evaluated on the MILK10k skin lesion dataset.
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Robust Fair Disease Diagnosis in CT Images
A combined logit-adjusted loss and CVaR objective improves macro F1 and reduces gender disparity in 3D CT classification of lung cancers, COVID-19, and normal cases on a benchmark with severe class and group imbalance.
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JI-ADF: Joint-Individual Learning with Adaptive Decision Fusion for Multimodal Skin Lesion Classification
JI-ADF is a trimodal model with joint-individual learning, multimodal fusion attention, and adaptive decision fusion evaluated on the MILK10k skin lesion dataset.