Efficient Knowledge Distillation for Brain Tumor Segmentation

Abstract

Deep learning has allowed great progress to be made in obtaining more accurate prediction results for brain tumor segmentation. The current mainstream research approaches obtain segmentation accuracy improvements by modifying deep-learning model architectures, while ignoring the computational and storage efficiency issues of segmentation. In this paper, we proposed an improved knowledge distillation method: coordinate distillation (CD), which integrates channel and space information and completes brain tumor segmentation by training the student network with the teacher network without changing the original network architecture. Experimental results showed that the method was effective and that it could enhance the segmentation accuracy of brain tumors without changing the segmentation efficiency.