JI-ADF fuses three modalities with adaptive decision fusion and a multimodal attention module to achieve balanced, well-calibrated performance on the imbalanced MILK10k skin lesion benchmark.
Seven-point checklist and skin lesion classification using multitask multimodal neural nets,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
JI-ADF: Joint-Individual Learning with Adaptive Decision Fusion for Multimodal Skin Lesion Classification
JI-ADF fuses three modalities with adaptive decision fusion and a multimodal attention module to achieve balanced, well-calibrated performance on the imbalanced MILK10k skin lesion benchmark.
-
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.