Modality-Agnostic Learning for Medical Image Segmentation Using Multi-modality Self-distillation

• URL: http://arxiv.org/abs/2306.03730v1
• Date: Tue, 6 Jun 2023 14:48:50 GMT
• Title: Modality-Agnostic Learning for Medical Image Segmentation Using Multi-modality Self-distillation
• Authors: Qisheng He, Nicholas Summerfield, Ming Dong, Carri Glide-Hurst
• Abstract summary: We propose a novel framework, Modality-Agnostic learning through Multi-modality Self-dist-illation (MAG-MS) MAG-MS distills knowledge from the fusion of multiple modalities and applies it to enhance representation learning for individual modalities. Our experiments on benchmark datasets demonstrate the high efficiency of MAG-MS and its superior segmentation performance.
• Score: 1.815047691981538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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