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PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification
[article]
2022
arXiv
pre-print
Imbalanced training data is a significant challenge for medical image classification. In this study, we propose a novel Progressive Class-Center Triplet (PCCT) framework to alleviate the class imbalance issue particularly for diagnosis of rare diseases, mainly by carefully designing the triplet sampling strategy and the triplet loss formation. Specifically, the PCCT framework includes two successive stages. In the first stage, PCCT trains the diagnosis system via a class-balanced triplet loss
arXiv:2207.04793v1
fatcat:reado6j5zfcn3mvcg2t2h6crgm