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Updated: May 13, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

FDSS-Net: feature enhancement and dual-stream semantic mixture network for polyp segmentation.

Weidong Wang¹, Xiaoxuan Mo², Junzhao Huang²

¹Xinjiang Second Medical College, Karamay, China. wangwd@cug.edu.cn.

Scientific Reports

|May 11, 2026

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A new network, FDSS-Net, improves polyp segmentation in colonoscopy images for colorectal cancer detection. It enhances accuracy by capturing multi-scale context and blending features, outperforming existing methods.

Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Accurate polyp segmentation is crucial for colorectal cancer early detection.
Existing methods struggle with polyp variability (shape, size) and low contrast.

Purpose of the Study:

Introduce FDSS-Net, a novel network architecture to enhance polyp segmentation accuracy in colonoscopy images.
Address limitations of current segmentation techniques.

Main Methods:

Developed FDSS-Net with three key modules: Feature Enhancement and Propagation Module (FEPM), Dual-Stream Semantic Mixture (DSSM), and Hierarchical Multi-scale Aggregation and Prediction Module (HMAP).
FEPM captures multi-scale context using depthwise separable convolutions.
DSSM aligns features and blends semantics across levels using cross-attention and global context.

Keywords:

Convolutional neural network Feature enhancement Polyp segmentation

Related Experiment Videos

Last Updated: May 13, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

HMAP aggregates features hierarchically with a learnable gate.

Main Results:

FDSS-Net outperformed 12 state-of-the-art methods on five datasets.
Achieved a Dice coefficient of 0.8302 and mIoU of 0.7587 on the ETIS-LaribPolypDB dataset.
Demonstrated superior performance in segmenting polyps with challenging characteristics.

Conclusions:

FDSS-Net significantly enhances polyp segmentation accuracy in colonoscopy images.
The proposed architecture shows potential for improving clinical computer-aided diagnosis systems.
Offers a promising direction for future research in medical image analysis.