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Modality-Agnostic Learning for Medical Image Segmentation Using Multi-modality Self-distillation
Medical image segmentation of tumors and organs at risk is a time-consuming yet critical process in the clinic that utilizes multi-modality imaging (e. g, different acquisitions, data types, and sequences) to increase segmentation precision.
Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach
As a deep learning model typically contains millions of trainable weights, there has been a growing demand for a more efficient network structure with reduced storage space and improved run-time efficiency.
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