3 accepted papers
In utilizing deep learning techniques for medical image segmentation, two types of imbalance issues are observed: inter-class imbalance between majority and minority classes and intra-class imbalance between easy and hard samples. However, existing loss functions typically confuse these issues, lead…
Although medical image segmentation has achieved remarkable results with supervised learning, obtaining labeled data remains challenging and costly. To counteract this, we present the MTSPSeg, a multi-task self-supervised learning framework. We establish the dynamic gradient learning rate (DGLR) str…
In the context of medical image segmentation, complementary information among multi-modality images can improve segmentation performance. However, acquiring the complete multi-modality data in clinical settings is difficult. To tackle this problem, we propose a novel multi-modality knowledge distill…