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Gestalt-Inspired Feature Integration Network with Entropy Uncertainty Modeling for Pathology Image Segmentation.

Dawei Fan, Jiamei Wen, Heng Dong

IEEE Journal of Biomedical and Health Informatics

|December 17, 2025

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This study introduces the Gestalt-Inspired Feature Integration Network (GeNet) for accurate pathology image segmentation. GeNet enhances cancer screening and tumor grading by effectively fusing global context and local details, improving model performance.

Area of Science:

Medical image analysis
Computational pathology
Artificial intelligence in healthcare

Background:

Pathology image segmentation is crucial for cancer screening and tumor grading.
Existing methods struggle with complex structures, uncertain regions, and feature fusion limitations.
Simplistic feature aggregation in current models hinders performance.

Purpose of the Study:

To develop a novel architecture for accurate and stable pathology image segmentation.
To improve feature fusion by integrating global context and local details synergistically.
To address challenges posed by complex local structures and uncertain regions in pathological images.

Main Methods:

Proposed a Gestalt-Inspired Feature Integration Network (GeNet) architecture.

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Implemented a mechanism to synergistically leverage multi-scale information based on feature similarity.

Utilized information entropy to quantify feature uncertainty and prioritize ambiguous regions.

Employed parallel convolutional recalibration in a refinement block to reduce feature redundancy and misalignment.

Main Results:

GeNet demonstrated high accuracy and strong robustness in pathological image segmentation.
The network effectively fuses global context with local details for improved performance.
Experiments on GlaS, GCaSeg, and EBHI-Seg datasets validated the model's efficacy.

Conclusions:

GeNet offers a novel approach to joint modeling of global and local features in medical image analysis.
The proposed architecture enhances accuracy and stability in pathology image segmentation.
This work provides a new perspective for improving clinical applications like cancer screening.