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Multi-interactive feature embedding learning for medical image segmentation.

Yijia Huang¹, Yue Luo²

¹School of Public Health, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.

Frontiers in Medicine

|October 10, 2025

Summary

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This study introduces a novel multi-interactive feature embedding learning framework for medical image segmentation. It enhances lesion detail capture by integrating reconstruction and segmentation tasks, improving accuracy.

Area of Science:

Medical Imaging Analysis
Computer Vision
Machine Learning

Background:

Medical image segmentation provides semantic lesion information but often misses crucial edge and texture details.
Medical image reconstruction can capture fine details, complementing segmentation by providing rich textural and structural information.

Purpose of the Study:

To propose a multi-interactive feature embedding learning framework that synergistically combines medical image reconstruction and segmentation.
To enhance the accuracy and detail-richness of medical image segmentation by leveraging complementary information from reconstruction tasks.

Main Methods:

Developed a novel framework integrating medical image reconstruction and segmentation tasks.
Introduced an adaptive feature modulation module for comprehensive feature aggregation.

Keywords:

adaptive feature modulation module bi-directional fusion module medical image segmentation multi-branch vision mamba self-supervised learning

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Proposed a bi-directional fusion module to integrate complementary features between tasks.

Utilized a multi-branch visual mamba for multi-scale structural information capture.

Main Results:

The proposed framework effectively bridges the gap in low-level details often lost in standard segmentation.
Demonstrated significant improvements in capturing lesion edge texture and structural information.
Achieved state-of-the-art performance across four diverse medical imaging datasets.

Conclusions:

The multi-interactive feature embedding learning approach offers a powerful solution for detailed medical image segmentation.
Integrating reconstruction and segmentation tasks enhances the understanding of lesion characteristics.
The framework shows strong adaptability and effectiveness for various medical imaging applications.