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Few-shot medical image segmentation with high-fidelity prototypes. | LitMetric

Few-shot medical image segmentation with high-fidelity prototypes.

Med Image Anal

Surrey Institute for People-Centred Artificial Intelligence, and Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK. Electronic address:

Published: February 2025

AI Article Synopsis

  • Few-shot Semantic Segmentation (FSS) allows pretrained models to learn new object classes with minimal labeled training data, but current methods struggle with complex backgrounds, particularly in medical imaging.
  • The proposed DetailSelf-refinedPrototypeNetwork (DSPNet) improves segmentation accuracy by creating detailed prototypes that better represent both foreground objects and background contexts.
  • Experimental results on challenging medical image datasets demonstrate that DSPNet outperforms existing state-of-the-art techniques, and the available code can be found at the provided GitHub link.

Article 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 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.

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Source
http://dx.doi.org/10.1016/j.media.2024.103412DOI Listing

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