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TGANet: Text-guided attention for improved polyp segmentation.

Nikhil Kumar Tomar1, Debesh Jha2, Ulas Bagci2

  • 1NepAL Applied Mathematics and Informatics Institute for Research (NAAMII), Kathmandu, Nepal.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|February 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a text-guided attention network (TGANet) to improve automated polyp segmentation in colonoscopies. The novel approach enhances deep learning models to accurately detect polyps of various sizes, reducing missed rates for early colon cancer detection.

Keywords:
Label embeddingattentionmulti-scale featurespolyp

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • Colonoscopy is essential for detecting colon cancer precursors but is operator-dependent.
  • Automated polyp segmentation aids early diagnosis but struggles with variable polyp sizes.
  • Existing deep learning models may underperform on differently sized polyps due to training data bias.

Purpose of the Study:

  • To develop an improved deep learning model for automated polyp segmentation.
  • To address the challenge of variable polyp sizes in colonoscopy images.
  • To enhance the accuracy and generalizability of polyp detection models.

Main Methods:

  • Exploited size-related and polyp number-related features using text attention during training.
  • Introduced an auxiliary classification task to weight text-based embeddings.
  • Developed a text-guided attention network (TGANet) for polyp segmentation.

Main Results:

  • The proposed TGANet demonstrated improved performance over state-of-the-art segmentation methods.
  • Text embeddings enhanced the model's ability to adapt to differently sized polyps and multiple polyps.
  • The model showed good generalization across four different datasets, with size-specific improvements.

Conclusions:

  • The text-guided attention network (TGANet) effectively improves automated polyp segmentation accuracy.
  • The approach successfully addresses the limitations of variable polyp sizes in deep learning models.
  • TGANet offers a promising solution for more reliable early-stage colon cancer detection through improved colonoscopy analysis.