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    WEAKLY SUPERVISED LEARNING WITH WHOLE SLIDE IMAGES
    5.
    发明申请
    WEAKLY SUPERVISED LEARNING WITH WHOLE SLIDE IMAGES 有权

    公开(公告)号：US20220180626A1

    公开(公告)日：2022-06-09

    申请号：US17605224

    申请日：2020-03-10

    申请人： NantOmics, LLC ,  NantHealth, Inc.

    发明人： Bing Song ,  Mustafa Jaber ,  Liudmila Beziaeva ,  Shahrooz Rabizadeh

    IPC分类号： G06V10/774 ,  G06V10/764 ,  G06V10/77 ,  G06T7/194 ,  G06V10/766 ,  G06V10/22 ,  G06V10/82 ,  G06V10/56 ,  G06V10/36 ,  G06T7/00 ,  G16H30/40 ,  G16H10/40

    摘要： Techniques are provided for determining classifications based on WSIs. A varied-size feature map is generated for each training WSI by generating a grid of patches for the training WSI, segmenting the training WSI into tissue and non-tissue areas, and converting patches comprising the tissue areas into tensors. Bounding boxes are generated based on the patches comprising tissue areas and segmented into feature map patches. A fixed-size feature map is generated based on a subset of the feature map patches. A classifier model is trained to process fixed-size feature maps corresponding to the training WSIs such that, for each fixed-size feature map, the classifier model is operable to assign a WSI-level tissue or cell morphology classification or regression based on the tensors. A classification engine is configured to use the trained classifier model to determine a WSI-level tissue or cell morphology classification or regression for a test WSI.

    DIGITAL HISTOPATHOLOGY AND MICRODISSECTION
    10.
    发明申请
    DIGITAL HISTOPATHOLOGY AND MICRODISSECTION 有权

    公开(公告)号：US20230129222A1

    公开(公告)日：2023-04-27

    申请号：US18088487

    申请日：2022-12-23

    申请人： NantOmics, LLC

    发明人： Bing Song ,  Gregory Chu

    IPC分类号： G06V10/82 ,  G06T7/00 ,  G06N3/04 ,  G06T7/187 ,  G06T7/11 ,  G06V10/50 ,  G06V10/44 ,  G06V20/69 ,  G06F18/21 ,  G06F18/20 ,  G06F18/2411 ,  G06F18/23213 ,  G06F18/2413 ,  G06F18/2415 ,  G06N3/045 ,  G06N7/01 ,  G06V10/764 ,  G06V10/778

    摘要： A computer implemented method of generating at least one shape of a region of interest in a digital image is provided. The method includes obtaining, by an image processing engine, access to a digital tissue image of a biological sample; tiling, by the image processing engine, the digital tissue image into a collection of image patches; identifying, by the image processing engine, a set of target tissue patches from the collection of image patches as a function of pixel content within the collection of image patches; assigning, by the image processing engine, each target tissue patch of the set of target tissue patches an initial class probability score indicating a probability that the target tissue patch falls within a class of interest, the initial class probability score generated by a trained classifier executed on each target tissue patch; generating, by the image processing engine, a first set of tissue region seed patches by identifying target tissue patches having initial class probability scores that satisfy a first seed region criteria, the first set of tissue region seed patches comprising a subset of the set of target tissue patches; generating, by the image processing engine, a second set of tissue region seed patches by identifying target tissue patches having initial class probability scores that satisfy a second seed region criteria, the second set of tissue region seed patches comprising a subset of the set of target tissue patches; calculating, by the image processing engine, a region of interest score for each patch in the second set of tissue region seed patches as a function of initial class probability scores of neighboring patches of the second set of tissue region seed patches and a distance to patches within the first set of issue region seed patches; and generating, by the image processing engine, one or more region of interest shapes by grouping neighboring patches based on their region of interest scores.