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CAENet: Contrast adaptively enhanced network for medical image segmentation based on a differentiable pooling

Shengke Li1, Yue Feng2, Hong Xu3

  • 1Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, 529020, Guangdong, China; School of Engineering, Guangzhou College of Technology and Business, Foshan, 528100, Guangdong, China.

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Summary

This study introduces a novel semantic segmentation network to improve medical image analysis. The Contrastive Adaptive Augmented Semantic Segmentation Network enhances feature extraction for better recognition of small targets in low-contrast medical images.

Keywords:
Channel attentionDeep supervisionDifferentiable pooling functionMedical imageSemantic segmentation

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Low-contrast medical images present challenges in semantic segmentation due to pixel confusion between classes.
  • Accurate segmentation is crucial for identifying small targets, which is often hindered by subtle differences.

Purpose of the Study:

  • To develop an advanced semantic segmentation network for improved medical image analysis.
  • To enhance the recognition of small targets and address challenges in low-contrast medical imaging.

Main Methods:

  • Proposed a Contrastive Adaptive Augmented Semantic Segmentation Network (CAASNet).
  • Introduced an Adaptive Contrast Augmentation module for high-frequency information extraction.
  • Implemented a Frequency-Efficient Channel Attention mechanism and a differentiable approximation of max pooling.

Main Results:

  • The proposed method demonstrated superior performance compared to state-of-the-art networks.
  • Effective on five diverse medical image datasets, including public and clinical data.
  • Successfully improved segmentation accuracy for small targets in low-contrast scenarios.

Conclusions:

  • The CAASNet effectively addresses challenges in medical image semantic segmentation.
  • The network shows significant potential for clinical applications in medical image analysis.
  • The proposed modules enhance detail extraction and reduce information loss during segmentation.