Enhancing Tiny Tissues Segmentation via Self-Distillation

Although the wide deployment of convolutional networks has greatly promoted the progress in the field of medical image segmentation, the performance of these method on tiny tissues, such as cell and fundus vessel, still needs to be improved. Most approaches focus on modifying the network architecture to overcome the problem of missing details in segmented images. In this paper, we try to solve this problem from a new perspective, that is, introducing self-distillation mechanism to fully utilize the features extracted from the network. Our method can be viewed as a combination of a novel loss function and a specific training strategy. It can be easily integrated into most existing encoderdecoder structured networks with few additional computational cost. We conduct experiments on four datasets, which are DRIVE, CHASEDB, GlaS and TNBC, and serval commonly used models to prove the effectiveness of our method. Experiments show that the performance of these models has been improved, which proves that our method is a general method and can be widely used in the field of m edical image segmentation.