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 DetailSelf-refinedPrototypeNetwork (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.

中文翻译：

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使用高保真原型进行小样本医学图像分割


Few-shot 语义分割 （FSS） 旨在使预训练模型适应新类，每个类只有一个标记的训练样本。尽管基于原型的方法已经取得了实质性的成功，但现有模型仅限于具有相当不同的对象且没有高度复杂背景的成像场景，例如自然图像。这使得此类模型在两种情况都无效的情况下，对于医学成像来说并不理想。为了解决这个问题，我们提出了一种新的 DetailSelf-refinedPrototypeNetwork （DSPNet），以更全面地构建代表对象前景和背景的高保真原型。具体来说，为了在保持捕获的细节语义的同时构建全局语义，我们通过使用聚类对多模态结构进行建模，然后以通道方式融合每个结构来学习前景原型。考虑到背景在空间维度上往往没有明显的语义关系，我们在稀疏通道感知调控下整合通道特异性结构信息。对三个具有挑战性的医学影像基准的广泛实验表明，DSPNet 优于以前的最先进方法。代码和数据可在 https://github.com/tntek/DSPNet 上获得。