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Lesion-Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale.

Jinzheng Cai, Adam P Harrison, Youjing Zheng

    IEEE Transactions on Medical Imaging
    |September 7, 2020
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    Summary

    This study introduces Lesion-Harvester, an automated system to find missing lesion annotations in medical images, significantly improving machine learning model training. It successfully identified thousands of new lesions with high precision, boosting detector performance.

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    Area of Science:

    • Medical Image Analysis
    • Machine Learning
    • Computer-Aided Diagnosis

    Background:

    • Acquiring large annotated medical image datasets for machine learning is costly and time-consuming.
    • Existing datasets like DeepLesion often have incomplete or noisy labels, hindering model training.
    • Effective methods for automatically harvesting or discovering missing annotations are crucial.

    Purpose of the Study:

    • To develop and validate Lesion-Harvester, a system for high-precision annotation harvesting from lesion datasets.
    • To improve the completeness and quality of medical image annotations for machine learning.
    • To enhance the performance of downstream machine learning models through augmented datasets.

    Main Methods:

    • A chained system combining a sensitive lesion proposal generator (LPG) and a selective lesion proposal classifier (LPC).
    • Iterative finetuning of the LPG using a novel hard negative suppression loss.
    • Development of a 3D contextual LPG and a global-local multi-view LPC for optimized performance.
    • Introduction of a pseudo 3D Intersection over Union (IoU) evaluation metric.

    Main Results:

    • Lesion-Harvester discovered an additional 9,805 lesions in the DeepLesion dataset with 90% precision.
    • Augmenting DeepLesion annotations with harvested lesions improved state-of-the-art detectors' average precision by 7-10%.
    • Public release of harvested lesions and a fully annotated DeepLesion test set.

    Conclusions:

    • Lesion-Harvester effectively addresses the challenge of incomplete annotations in medical imaging datasets.
    • The system significantly enhances the utility of existing datasets for machine learning model development.
    • The released data and methods facilitate further research in automated medical image annotation and analysis.