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Updated: Jun 27, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

PolyMamba-Net: a lightweight and boundary-aware network for real-time polyp segmentation in colonoscopy.

Weiyan Yuan¹, Yuyang Cai², Weiwei Wang³

¹Department of Gastroenterology, Nantong First People's Hospital, Nantong, China.

Frontiers in Medicine

|June 26, 2026

Summary

This summary is machine-generated.

This study introduces PolyMamba-Net, a novel deep learning model for accurate colorectal cancer polyp segmentation. The model achieves high precision and real-time performance, aiding endoscopists in reducing missed diagnoses during colonoscopies.

Keywords:

deep learning medical image analysis polyp segmentation real-time diagnosis state space models

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Last Updated: Jun 27, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Colorectal cancer (CRC) is a major global health concern, with early detection crucial for survival.
Colonoscopy is key for identifying and removing precancerous polyps, but missed diagnoses, especially of flat polyps, remain an issue.
Current deep learning models for polyp segmentation struggle to balance accuracy with the real-time processing needed for clinical use.

Purpose of the Study:

To develop an efficient and accurate deep learning model for automated polyp segmentation in colonoscopy images.
To address the limitations of existing models in handling subtle polyp appearances and achieving real-time inference speeds.

Main Methods:

Proposed PolyMamba-Net, a hybrid architecture combining State Space Models (Mamba) for long-range dependencies and Convolutional Neural Networks (CNNs) for local features.
Introduced a dual-branch encoder for global and local feature extraction and a Boundary-Aware Module (BAM) for precise polyp margin delineation.
Utilized a composite loss function for structural, pixel-level, and boundary consistency during training.

Main Results:

PolyMamba-Net achieved high Dice Coefficients (0.942 on Kvasir-SEG, 0.935 on CVC-ClinicDB), outperforming state-of-the-art methods including recent 2024-2025 approaches.
Demonstrated statistically significant improvements (p < 0.05) over competitors across all metrics.
Achieved real-time performance (115 FPS) with a compact model size (25.3M parameters, 12.8 GFLOPs), surpassing transformer-based models in efficiency.

Conclusions:

PolyMamba-Net offers a clinically viable solution for enhancing polyp detection during colonoscopies.
The model's high segmentation accuracy and real-time processing capabilities can significantly assist endoscopists in reducing missed polyp diagnoses.