  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.
