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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
    1.
    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

    申请人： Arizona Board of Regents on behalf of Arizona State University

    发明人： DongAo Ma ,  Jiaxuan Pang ,  Jianming Liang

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

    CPC分类号： G16H30/40 ,  G06V10/764 ,  G16H30/20

    摘要： 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.

    SYSTEMS, METHODS, AND APPARATUSES FOR THE GENERATION OF SELF-TAUGHT MODELS GENESIS ABSENT MANUAL LABELING FOR THE PROCESSING OF MEDICAL IMAGING
    5.
    发明申请
    SYSTEMS, METHODS, AND APPARATUSES FOR THE GENERATION OF SELF-TAUGHT MODELS GENESIS ABSENT MANUAL LABELING FOR THE PROCESSING OF MEDICAL IMAGING 有权

    公开(公告)号：US20210326653A1

    公开(公告)日：2021-10-21

    申请号：US17224886

    申请日：2021-04-07

    申请人： Arizona Board of Regents on Behalf of Arizona State University

    发明人： Zongwei Zhou ,  Vatsal Sodha ,  Jiaxuan Pang ,  Jianming Liang

    IPC分类号： G06K9/62 ,  G06T3/00 ,  G06N3/04 ,  G06N3/08 ,  G16H30/40

    摘要： Described herein are means for generation of self-taught generic models, named Models Genesis, without requiring any manual labeling, in which the Models Genesis are then utilized for the processing of medical imaging. For instance, an exemplary system is specially configured for learning general-purpose image representations by recovering original sub-volumes of 3D input images from transformed 3D images. Such a system operates by cropping a sub-volume from each 3D input image; performing image transformations upon each of the sub-volumes cropped from the 3D input images to generate transformed sub-volumes; and training an encoder-decoder architecture with skip connections to learn a common image representation by restoring the original sub-volumes cropped from the 3D input images from the transformed sub-volumes generated via the image transformations. A pre-trained 3D generic model is thus provided, based on the trained encoder-decoder architecture having learned the common image representation which is capable of identifying anatomical patterns in never before seen 3D medical images having no labeling and no annotation. More importantly, the pre-trained generic models lead to improved performance in multiple target tasks, effective across diseases, organs, datasets, and modalities.