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A Novel Application of Musculoskeletal Ultrasound Imaging
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Source-Resilient Joint Learning Framework for Preserving Stable Generalization on Diverse Ultrasonic Source

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    |October 23, 2025
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    Summary
    This summary is machine-generated.

    A new source-resilient joint learning framework enhances generalization across diverse ultrasonic scenarios by unifying data sources and improving feature consistency. This approach overcomes limitations of previous methods, boosting performance in segmentation and classification tasks.

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    Area of Science:

    • Medical imaging
    • Machine learning
    • Computer vision

    Background:

    • Joint learning on diverse ultrasonic data faces challenges due to source heterogeneity and feature inconsistency, limiting generalization.
    • Existing methods lack source-resilience, leading to performance degradation with varied ultrasound data.
    • Limited single-source data variations and ultrasound imaging interference further hinder generalization.

    Purpose of the Study:

    • To propose a novel source-resilient joint learning framework to enhance generalization in diverse ultrasonic source scenarios.
    • To address data heterogeneity, feature inconsistency, limited data variation, and ultrasound interference.
    • To provide a generalizable framework for improving joint learning performance in medical imaging.

    Main Methods:

    • A three-stage framework: source transforming (1-to-N transformation), feature enhancement (manifold-constraint normalization module, task-consistent attention module, adaptive feature-shifting module), and ultrasound-hybrid linear mapping (speckle randomization, Monge-Kantorovitch linear mapping).
    • Manifold-constraint normalization module (MCNM) minimizes manifold-based loss to address heterogeneity.
    • Task-consistent attention module (TCAM) uses self-attention for multi-scale feature sharing to tackle inconsistency.
    • Adaptive feature-shifting module (AFSM) provides feature-level augmentation for limited single-source data.
    • Ultrasound-hybrid linear mapping (USmapping) randomizes ultrasonic style to mitigate imaging interference.

    Main Results:

    • The framework achieved superior performance on eight ultrasound datasets across multiple centers and scanners.
    • Segmentation task: Weighted average Dice Similarity Coefficient (DSCWAvg) of 75.7%.
    • Classification task: Weighted average Area Under the Receiver Operating Characteristic curve (AUROCWAvg) of 68.8%.

    Conclusions:

    • The proposed source-resilient joint learning framework effectively enhances generalization in diverse ultrasonic scenarios.
    • The framework demonstrates significant improvements over previous methods in both segmentation and classification tasks.
    • This approach holds potential as a general solution for robust joint learning in challenging medical imaging applications.