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.

本研究提出了一种新的框架，Modality-Agnostic learning through Multi-modality Self-dist-illation (MAG-MS)，用于研究输入多模式的影响，以增强个体模式的表示学习，从而提高医学图像分割的效率和准确性，并提供了在选择输入模式方面有价值的见解和指导。