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RTA-Former: Reverse Transformer Attention for Polyp Segmentation.

Zhikai Li, Murong Yi, Ali Uneri

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|March 3, 2025

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This study introduces RTA-Former, a novel deep learning network for polyp segmentation. RTA-Former enhances edge segmentation accuracy, improving colorectal cancer early detection and clinical decisions.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Polyp segmentation is crucial for colorectal cancer prevention and early detection.
Deep learning aids in automating polyp diagnosis, but accurate edge segmentation remains a challenge.

Purpose of the Study:

To introduce RTA-Former, a novel network for enhanced polyp segmentation.
To address limitations in current deep learning models for accurate polyp edge detection.

Main Methods:

Developed RTA-Former, a transformer-based network.
Incorporated Reverse Attention (RA) with a transformer decoder stage.
Evaluated performance on five polyp segmentation datasets.

Main Results:

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RTA-Former achieved state-of-the-art (SOTA) performance across five datasets.
Demonstrated superior accuracy in polyp edge segmentation.
Validated the effectiveness of the novel Reverse Attention mechanism.

Conclusions:

RTA-Former significantly improves Transformer-based polyp segmentation accuracy.
The enhanced segmentation capability promises better clinical decisions and patient outcomes.
Publicly available code facilitates further research and application.