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PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation
In weakly supervised medical image segmentation, the absence of structural priors and the discreteness of class feature distribution present a challenge, i. e., how to accurately propagate supervision signals from local to global regions without excessively spreading them to other irrelevant regions?
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