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VGFA: Variation-Robust Graph-Level Feature Alignment for Domain Adaptive Nuclei Detection.

Kai Fan, Zhi Wang, Aiqiu Wu

    IEEE Transactions on Medical Imaging
    |December 18, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel framework for robust nuclei detection in pathology images, addressing domain discrepancies and intra-domain variations. The method enhances accuracy in disease diagnosis by improving unsupervised domain adaptation for nuclei detection models.

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    Area of Science:

    • Digital Pathology
    • Computational Biology
    • Medical Image Analysis

    Background:

    • Accurate nuclei detection is vital for pathology image analysis in disease diagnosis and treatment.
    • Domain discrepancies and intra-domain variations (IDV) in pathology images significantly degrade detection model performance.
    • Existing domain adaptation methods often overlook the impact of IDV at both cell and image scales.

    Purpose of the Study:

    • To propose a novel variation-robust graph-level feature alignment (VGFA) framework for unsupervised domain adaptive nuclei detection.
    • To address challenges posed by significant domain discrepancies and intra-domain variations in pathology images.
    • To improve the performance and robustness of nuclei detection models in diverse pathological datasets.

    Main Methods:

    • Developed a variation-robust graph-level feature alignment (VGFA) framework.
    • Incorporated a prior-based nuclei graph pruning scheme to eliminate unreliable nodes.
    • Designed a local-global nuclei encoding network for holistic nuclei graph representation learning.
    • Utilized a nuclei graph discrepancy loss resilient to image-scale IDV for cross-domain feature alignment.

    Main Results:

    • The VGFA framework demonstrated state-of-the-art performance in unsupervised domain adaptive nuclei detection.
    • Achieved superior results compared to existing feature alignment methods across various adaptation scenarios.
    • Effectively mitigated the detrimental impact of intra-domain variation at both cell and image scales.

    Conclusions:

    • The proposed VGFA framework offers a robust solution for nuclei detection in the presence of significant domain discrepancies and intra-domain variations.
    • VGFA enhances the reliability and accuracy of nuclei detection models in digital pathology.
    • This advancement contributes to more precise disease diagnosis and treatment planning through improved image analysis.