Modality-Agnostic Learning for Medical Image Segmentation Using Multi-modality Self-distillation
This work addresses the challenge of handling limited modalities in medical image segmentation for clinical applications, offering an efficient approach with practical guidance for modality selection.
The paper tackles the problem of medical image segmentation by proposing a modality-agnostic learning framework that distills knowledge from multiple modalities to enhance individual ones, achieving superior segmentation performance compared to state-of-the-art methods on benchmark datasets.
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. In this paper, we propose a novel framework, Modality-Agnostic learning through Multi-modality Self-dist-illation (MAG-MS), to investigate the impact of input modalities on medical image segmentation. MAG-MS distills knowledge from the fusion of multiple modalities and applies it to enhance representation learning for individual modalities. Thus, it provides a versatile and efficient approach to handle limited modalities during testing. Our extensive experiments on benchmark datasets demonstrate the high efficiency of MAG-MS and its superior segmentation performance than current state-of-the-art methods. Furthermore, using MAG-MS, we provide valuable insight and guidance on selecting input modalities for medical image segmentation tasks.