Few-Shot Medical Image Segmentation with High-Fidelity Prototypes
This work addresses the challenge of few-shot segmentation for medical imaging, which is incremental as it adapts existing prototype methods to a more complex domain.
The paper tackled the problem of few-shot semantic segmentation in medical imaging, where existing prototype-based models are suboptimal due to complex backgrounds and indistinct objects, by proposing a Detail Self-refined Prototype Network (DSPNet) that constructs high-fidelity prototypes, achieving superior performance over state-of-the-art methods on three challenging benchmarks.
Few-shot Semantic Segmentation (FSS) aims to adapt a pretrained model to new classes with as few as a single labelled 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 Detail Self-refined Prototype Network (DSPNet) to constructing 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 modelling the multi-modal 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.