OVARIAN TOXICITY ASSESSMENT IN HISTOPATHOLOGICAL IMAGES USING DEEP LEARNING

    公开(公告)号:US20220036548A1

    公开(公告)日:2022-02-03

    申请号:US17501712

    申请日:2021-10-14

    Inventor: Fang-Yao HU

    Abstract: The present disclosure relates to a deep learning neural network that can identify corpora lutea in the ovaries and a rules-based technique that can count the corpora lutea identified in the ovaries and infer an ovarian toxicity of a compound based on the count of the corpora lutea (CL). Particularly, aspects of the present disclosure are directed to obtaining a set of images of tissue slices from ovaries treated with an amount of a compound; generating, using a neural network model, the set of images with a bounding box around objects that are identified as the CL within the set of images based on coordinates predicted for the bounding box; counting the bounding boxes within the set of images to obtain a CL count for the ovaries; and determining an ovarian toxicity of the compound at the amount based on the CL count.

    Methods and compositions for prognosis and treatment of cancers

    公开(公告)号:US11236394B2

    公开(公告)日:2022-02-01

    申请号:US15609473

    申请日:2017-05-31

    Abstract: The invention provides methods of using expression levels of one or more immune cell gene signatures and/or combinations of immune cell gene signatures as selection criteria for selecting a patient having cancer for treatment with an immunotherapy. The invention further provides methods for selecting a patient having cancer who may benefit from a particular immunotherapy, such as an activating immunotherapy or a suppressing immunotherapy and administering to the patient the activating immunotherapy or suppressing immunotherapy to treat the cancer.

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