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    SYSTEMS, METHODS, AND APPARATUSES FOR ACCRUING AND REUSING KNOWLEDGE (ARK) FOR SUPERIOR AND ROBUST PERFORMANCE BY A TRAINED AI MODEL FOR USE WITH MEDICAL IMAGE CLASSIFICATION
    31.
    发明公开
    SYSTEMS, METHODS, AND APPARATUSES FOR ACCRUING AND REUSING KNOWLEDGE (ARK) FOR SUPERIOR AND ROBUST PERFORMANCE BY A TRAINED AI MODEL FOR USE WITH MEDICAL IMAGE CLASSIFICATION 审中-公开

    公开(公告)号：US20240339200A1

    公开(公告)日：2024-10-10

    申请号：US18627831

    申请日：2024-04-05

    Applicant: Arizona Board of Regents on behalf of Arizona State University

    Inventor： DongAo Ma ,  Jiaxuan Pang ,  Jianming Liang

    IPC: G16H30/40 ,  G06V10/764 ,  G16H30/20

    CPC classification number: G16H30/40 ,  G06V10/764 ,  G16H30/20

    Abstract: Exemplary systems include means for receiving medical image data at the system from a plurality of datasets provided via publicly available sources; evaluating the medical image data for the presence of expert notation embedded within the medical image data; determining the expert notations embedded within the medical image data are formatted using inconsistent and heterogeneous labeling across the plurality of datasets; generating an interim AI model by applying a task head classifier to learn the annotations of the expert notations embedded within the medical image data to generate an interim AI model; scaling the interim AI model having the learned annotations of the expert notations embedded therein to additional tasks by applying multi-task heads using cyclical pre-training of the interim AI model trained previously to generate task-specific AI models, with each respective task-specific AI model having differently configured task-specific learning objectives; training a pre-trained AI model specially configured for an application-specific target task by applying task re-visitation training forcing the pre-trained AI model being trained to re-visit all tasks in each round of training and forcing the pre-trained AI model being trained to re-use all accrued knowledge to improve learning by the pre-trained AI model being trained against the current application-specific target task for which the pre-trained AI model is being trained.

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