DEGL: dual-entropy guided loss with dynamic temperature regularization for long-tailed and imbalance-prone medical image classification
DEGL is proposed, a plug-and-play objective that aligns model confidence with dataset-level uncertainty during training and improves reliability and agreement metrics on imbalance-prone subsets while remaining competitive with representative calibration-oriented losses, including Focal, TET, and MiT.