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<p>Colorectal cancer prevention benefits from accurate and reproducible polyp segmentation, yet cross-domain generalization and boundary precision remain challenging in real-world deployments. We propose Prysm-Net, a ViT-based framework designed to address these issues through architectural innovation and advanced training guidance. Our model is augmented with a biologically inspired salience module (BSM) that dynamically sharpens boundary-relevant features. To further enhance robustness without increasing inference costs, we introduce two training-only strategies: (i) foundation-model distillation from SAM, which transfers knowledge at the output, boundary, and feature levels, and (ii) multi-modal guidance that injects auxiliary structural and textural cues via gated cross-attention. Extensive experiments on standard in-domain benchmarks and challenging cross-domain datasets demonstrate that Prysm-Net achieves superior segmentation accuracy and robust generalization compared to state-of-the-art methods, all while maintaining a lightweight inference process by disabling auxiliary guidance at test time.</p>

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PrysmNet a polyp refining system using salience and multimodal guidance for reproducible cross domain segmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月21日

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  • Junbo Xiao,
  • Yi Han,
  • Lei Wang,
  • Ying Li,
  • Xiaotong Wang,
  • Shizhe Li,
  • Jun Yi,
  • Yu Wu,
  • Xiaowei Liu

摘要

Colorectal cancer prevention benefits from accurate and reproducible polyp segmentation, yet cross-domain generalization and boundary precision remain challenging in real-world deployments. We propose Prysm-Net, a ViT-based framework designed to address these issues through architectural innovation and advanced training guidance. Our model is augmented with a biologically inspired salience module (BSM) that dynamically sharpens boundary-relevant features. To further enhance robustness without increasing inference costs, we introduce two training-only strategies: (i) foundation-model distillation from SAM, which transfers knowledge at the output, boundary, and feature levels, and (ii) multi-modal guidance that injects auxiliary structural and textural cues via gated cross-attention. Extensive experiments on standard in-domain benchmarks and challenging cross-domain datasets demonstrate that Prysm-Net achieves superior segmentation accuracy and robust generalization compared to state-of-the-art methods, all while maintaining a lightweight inference process by disabling auxiliary guidance at test time.