Prototype Knowledge Distillation for Medical Segmentation with Missing Modality

Shuai Wang1; Zipei Yan2; Daoan Zhang3; Haining Wei1; Zhongsen Li1; Rui Li1

10.1109/ICASSP49357.2023.10095014

2023

1520-6149

978-1-7281-6328-4

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

2023-June

1-5

4-10 June 2023

Rhodes Island, Greece

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.

Missing Modality Knowledge Distillation Medical Image Segmentation

其他

EI

20234715104852

IEEE

1.Tsinghua University
2.The Hong Kong Polytechnic University
3.Southern University of Science and Technology

Shuai Wang,Zipei Yan,Daoan Zhang,et al. Prototype Knowledge Distillation for Medical Segmentation with Missing Modality[C],2023:1-5.