Multi-shot Prototype Contrastive Learning and Semantic Reasoning for Medical Image Segmentation

Abstract

Despite the remarkable achievements made by deep convolutional neural networks in medical image segmentation, the limitation that they rely heavily on high-precision and intensively annotated samples makes it difficult to adapt to novel classes that have not been seen before. Few-shot learning is introduced to solve these challenges by learning the generalized representation of a semantic class from very few annotated support samples that can be used as a reference for unannotated query samples. In this paper, instead of averaging multiple support prototypes, we propose a multi-shot prototype contrastive learning and semantic reasoning network (MPSNet) for medical image segmentation. The multi-shot learning network exists independently within the support set, obtains effective semantic features for support images and gives priority to training the core segmentation model of prototype contrastive learning. We also propose a semantic reasoning network that takes the prior semantic features and prior segmentation model learned from the support set as the immediate and necessary conditions for the query image to deduce its segmentation mask. The proposed method is verified to be superior to the state-of-the-art methods on three public datasets, revealing its powerful segmentation and generalization abilities. Code: https://github.com/H51705/FSS_MPSNet.