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Complementary Information Mutual Learning for Multimodality Medical Image Segmentation
CIML adopts the idea of addition and removes inter-modal redundant information through inductive bias-driven task decomposition and message passing-based redundancy filtering.
Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration
Based on the confidential information, a self-adaptive reward function is designed to provide more detailed feedback, and a simulated label generation mechanism is proposed on unsupervised data to reduce over-reliance on labeled data.
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