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    Graph-Enhanced Visual Prompting (GEVP) addresses data limitations in medical imaging by using graph learning to create effective visual prompts. This method improves Vision Transformer (ViT) performance on diagnostic tasks, even with limited clinical data.

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    Area of Science:

    • Artificial Intelligence
    • Medical Imaging
    • Computer Vision

    Background:

    • Adapting Vision Transformers (ViTs) for medical imaging faces challenges due to limited data and annotations, impacting training and generalization.
    • Visual prompt learning offers parameter-efficient domain adaptation but requires task-specific semantic guidance, which is often unavailable in clinical settings.
    • There is a need for automated methods to extract reliable semantic cues from existing clinical data for effective prompt generation.

    Purpose of the Study:

    • To introduce Graph-Enhanced Visual Prompting (GEVP), a novel framework for generating semantically rich prompts in medical imaging.
    • To integrate cross-modal graph learning into prompt generation for Vision Transformers (ViTs) in medical applications.
    • To enable robust medical image analysis and disease classification, particularly in data-scarce environments.

    Main Methods:

    • GEVP models image patches and clinical report tokens as nodes in a graph structure.
    • A graph neural network is employed to capture spatial and semantic relationships between these nodes.
    • Generated prompts are injected into a frozen ViT backbone to guide attention to diagnostically relevant regions without extensive fine-tuning.

    Main Results:

    • GEVP demonstrated superior performance compared to existing prompt- and adapter-based methods on six public medical imaging datasets.
    • The framework achieved up to a +9.65% improvement in F1 score on imbalanced tasks.
    • GEVP showed enhanced capabilities in classifying unseen diseases, highlighting its generalization potential.

    Conclusions:

    • Graph-Enhanced Visual Prompting (GEVP) effectively overcomes data scarcity in medical imaging by leveraging cross-modal graph learning for prompt generation.
    • The proposed method enables parameter-efficient adaptation of Vision Transformers (ViTs) for medical image analysis, improving diagnostic accuracy.
    • GEVP offers a robust solution for both report-available and report-absent scenarios, paving the way for wider clinical adoption.