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TFCNet: A texture-aware and fine-grained feature compensated polyp detection network.

Xiaoying Pan1, Yaya Mu1, Chenyang Ma1

  • 1Shanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an, 710121, China; School of Computer Science & Technology, Xi'an University of Post & Telecommunications, Xi'an, 710121, China.

Computers in Biology and Medicine
|February 21, 2024
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Summary

A new texture-aware network (TFCNet) improves intestinal polyp detection by enhancing fine-grained features and reducing missed detections. This method boosts accuracy in early colorectal cancer diagnosis.

Keywords:
Colorectal cancerConvolutional neural networksFine-grained feature compensationPolyp detectionTexture awareness

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Area of Science:

  • Medical Image Analysis
  • Computer-Aided Diagnosis
  • Colorectal Cancer Research

Background:

  • Accurate abnormal tissue detection is crucial for medical image analysis and computer-aided diagnosis.
  • Convolutional Neural Networks (CNNs) show promise for intestinal polyp detection, aiding early colorectal cancer diagnosis.
  • Existing multi-scale feature processing models for polyp detection suffer from feature misalignment, leading to missed and false detections.

Purpose of the Study:

  • To address limitations in current polyp detection methods, this study proposes a novel Texture-Aware and Fine-Grained Feature Compensated Polyp Detection Network (TFCNet).
  • The goal is to improve the accuracy and reliability of intestinal polyp detection by preserving fine-grained features and semantic consistency.

Main Methods:

  • The TFCNet incorporates a Texture Awareness Module (TAM) to extract rich texture information from low-level layers and suppress background using high-level semantics.
  • A Texture Feature Enhancement Module (TFEM) refines low-level texture features and fuses them with high-level features, ensuring feature integrity.
  • A Residual Pyramid Splittable Attention Module (RPSA) is employed to mitigate channel information loss from skip connections, enhancing overall network performance.

Main Results:

  • TFCNet demonstrated superior performance across four datasets compared to existing methods.
  • On the PolypSets dataset, TFCNet achieved an mAP@0.5-0.95 of 88.9%.
  • Significant improvements were observed on smaller datasets: a 2% increase on CVC-ClinicDB and a 1.6% increase on Kvasir, highlighting TFCNet's effectiveness.

Conclusions:

  • The proposed TFCNet effectively compensates for fine-grained feature loss and improves polyp detection accuracy.
  • The integration of texture awareness and feature enhancement modules leads to more robust and reliable polyp detection.
  • TFCNet represents a significant advancement in computer-aided diagnosis for colorectal cancer, outperforming current state-of-the-art methods.