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    TextoMorph generates realistic synthetic tumors using text descriptions, improving AI performance on challenging cases like early tumor detection and classification. This approach enhances AI sensitivity and segmentation accuracy by leveraging radiology reports.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computational Pathology

    Background:

    • AI models struggle with diverse tumor characteristics, leading to missed detections or over-detection.
    • Existing tumor synthesis methods lack control over crucial features like texture and heterogeneity.
    • This limits their effectiveness in addressing AI weaknesses in medical imaging analysis.

    Purpose of the Study:

    • To introduce TextoMorph, a novel text-driven tumor synthesis approach.
    • To enable precise control over synthetic tumor characteristics using textual descriptions and masks.
    • To improve AI performance on challenging medical cases by generating targeted synthetic data.

    Main Methods:

    • Developed a text-driven tumor synthesis method (TextoMorph) incorporating mask control.
    • Utilized text mined from radiology reports to guide tumor generation.
    • Employed contrastive learning across texts and CT scans, leveraging a large corpus of reports and limited image-report pairs.
    • Conducted rigorous tests to evaluate the realism and diversity of synthetic tumors.

    Main Results:

    • TextoMorph significantly improved AI performance in key areas: +6.5% Sensitivity in early tumor detection, +3.1% in tumor segmentation for radiotherapy (NSD), and +8.2% Sensitivity in benign vs. malignant classification.
    • The approach increased variability and controllability of synthetic tumors, effectively targeting AI failure cases.
    • Demonstrated realistic and diverse synthetic tumors in texture, heterogeneity, boundary, and pathology.
    • Reduced dependence on scarce image-report pairs through effective use of a large report corpus.

    Conclusions:

    • TextoMorph offers a powerful solution for generating controllable and diverse synthetic tumors.
    • This text-driven approach enhances AI diagnostic capabilities in medical imaging, particularly for challenging cases.
    • The method shows promise for improving AI accuracy in tumor detection, segmentation, and classification while minimizing the need for extensive annotated datasets.