Prototype Knowledge Distillation for Medical Segmentation with Missing Modality

• URL: http://arxiv.org/abs/2303.09830v2
• Date: Mon, 17 Apr 2023 07:53:32 GMT
• Title: Prototype Knowledge Distillation for Medical Segmentation with Missing Modality
• Authors: Shuai Wang, Zipei Yan, Daoan Zhang, Haining Wei, Zhongsen Li, Rui Li
• Abstract summary: We propose a prototype knowledge distillation (ProtoKD) method to tackle the challenging problem. Our ProtoKD can not only distillate the pixel-wise knowledge of multi-modality data to single-modality data but also transfer intra-class and inter-class feature variations. Our method achieves state-of-the-art performance on BraTS benchmark.
• Score: 5.0043036421429035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modality medical imaging is crucial in clinical treatment as it can provide complementary information for medical image segmentation. However, collecting multi-modal data in clinical is difficult due to the limitation of the scan time and other clinical situations. As such, it is clinically meaningful to develop an image segmentation paradigm to handle this missing modality problem. In this paper, we propose a prototype knowledge distillation (ProtoKD) method to tackle the challenging problem, especially for the toughest scenario when only single modal data can be accessed. Specifically, our ProtoKD can not only distillate the pixel-wise knowledge of multi-modality data to single-modality data but also transfer intra-class and inter-class feature variations, such that the student model could learn more robust feature representation from the teacher model and inference with only one single modality data. Our method achieves state-of-the-art performance on BraTS benchmark. The code is available at \url{https://github.com/SakurajimaMaiii/ProtoKD}.

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