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Self-supervised Learning of Morphological Representation for 3D EM Segments with Cluster-Instance Co images brings significant challenges for cell segmentation and analysis. While obtaining sufficient data annotation for supervised deep learning methods is laborious and tedious, we propose the first self-supervised approach for learning 3D morphology representations from ultra-scale EM segments wiOverride 发表于 2025-4-2 02:48:21
Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentationes is expertise-demanded and time-consuming; hence semi-supervised learning (SSL), particularly barely-supervised learning, is highly desirable for training with insufficient labeled data. We observed that the class imbalance problem is severe in the knee MR images as the cartilages only occupy 6% o