Few-shot medical image segmentation with high-fidelity prototypes
Song Tang, Shaxu Yan, Xiaozhi Qi, Jianxin Gao, Mao Ye, Jianwei Zhang, Xiatian Zhu
Abstract
Few-shot Semantic Segmentation (FSS) aims to adapt a pretrained model to new classes with as few as a single labeled training sample per class. Despite the prototype based approaches have achieved substantial success, existing models are limited to the imaging scenarios with considerably distinct objects and not highly complex background, e.g., natural images. This makes such models suboptimal for medical imaging with both conditions invalid. To address this problem, we propose a novel D etail S elf-refined P rototype Net work ( DSPNet ) to construct high-fidelity prototypes representing the object foreground and the background more comprehensively. Specifically, to construct global semantics while maintaining the captured detail semantics, we learn the foreground prototypes by modeling the multimodal structures with clustering and then fusing each in a channel-wise manner. Considering that the background often has no apparent semantic relation in the spatial dimensions, we integrate channel-specific structural information under sparse channel-aware regulation. Extensive experiments on three challenging medical image benchmarks show the superiority of DSPNet over previous state-of-the-art methods. The code and data are available at https://github.com/tntek/DSPNet . • A novel prototypical FSS approach DSPNet that enhances prototypes’ self-representation. • A class prototype self-refining method FSPA integrating the cluster prototypes. • A background prototype self-refining method BCMA coding channel-specific structure.