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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.
Dual self-distillation of U-shaped networks for 3D medical image segmentation
U-shaped networks and its variants have demonstrated exceptional results for medical image segmentation.
SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT Generation
In medical image synthesis, model training could be challenging due to the inconsistencies between images of different modalities even with the same patient, typically caused by internal status/tissue changes as different modalities are usually obtained at a different time.
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