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Related Experiment Videos

SCA-Net: A Scale- and Contrast-Aware Network for Subtle and Low-Contrast Polyp Segmentation.

Jiaxu Huang1, Yiyue Li1, Jiaqi Zhang1

  • 1Department of Computer Science, Jiangsu University, No. 301 Xuefu Road, Zhenjiang, 212013, Jiangsu, China.

Journal of Imaging Informatics in Medicine
|July 7, 2026
PubMed
Summary

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SCA-Net improves polyp segmentation for colorectal cancer detection by enhancing semantic representation and boundary sensitivity. This novel network shows improved performance, especially on challenging datasets, aiding early diagnosis.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate polyp segmentation is crucial for early colorectal cancer detection.
  • Existing methods face challenges with subtle polyps, weak boundaries, and cross-dataset generalization.

Purpose of the Study:

  • To propose SCA-Net, a scale- and contrast-aware network for enhanced polyp segmentation.
  • To improve semantic representation, scale adaptability, and boundary sensitivity in polyp segmentation.

Main Methods:

  • Developed SCA-Net, a unified encoder-decoder framework.
  • Introduced a semantic module group (SMG) with cross-scale global aggregator (CSGA) and gated semantic injection (GSI).
  • Implemented a size-adaptive dynamic router (SADR) and a Laplacian-guided synergistic refiner (LGSR).
Keywords:
Boundary sensitivityCross-dataset generalizationPolyp segmentationScale adaptabilitySemantic representation

Related Experiment Videos

Main Results:

  • SCA-Net demonstrated competitive performance on seen datasets.
  • Achieved significant gains on challenging unseen benchmarks, including ETIS-LaribPolypDB.
  • Attained 86.0% Dice score on ETIS-LaribPolypDB with a PVTv2-B4 backbone.

Conclusions:

  • SCA-Net effectively addresses limitations in existing polyp segmentation methods.
  • The proposed network enhances scale adaptability and boundary refinement for improved accuracy.
  • SCA-Net shows promise for advancing early colorectal cancer detection through improved segmentation.