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    SYSTEMS AND METHODS TO PROCESS ELECTRONIC IMAGES TO IDENTIFY ABNORMAL MORPHOLOGIES
    2.
    发明公开
    SYSTEMS AND METHODS TO PROCESS ELECTRONIC IMAGES TO IDENTIFY ABNORMAL MORPHOLOGIES 审中-公开

    公开(公告)号：US20230196583A1

    公开(公告)日：2023-06-22

    申请号：US18051352

    申请日：2022-10-31

    Applicant: PAIGE.AI, INC.

    Inventor： Ran GODRICH ,  Christopher KANAN

    IPC: G06T7/194 ,  G06T7/00

    CPC classification number: G06T7/194 ,  G06T7/0014 ,  G06T2207/30024 ,  G06T2207/20084 ,  G06T2207/20081

    Abstract: Systems and methods for identifying morphologies present in digital whole slide images. The method may include receiving one or more digital whole slide images associated with a patient; determining a plurality of foreground tiles within the one or more digital whole slide images associated with a patient; determining, using a trained machine learning model, whether each foreground tile of the plurality of foreground tiles contains a known morphology or an unknown morphology; upon determining that one or more foreground tiles contains an unknown morphology, providing the one or more foreground tiles with an unknown morphology to a clustering algorithm, the clustering algorithm associating each of the one or more tiles with an unknown morphology cluster; and based on the associated unknown morphology cluster, predicting at least one outcome for the patient.

    SYSTEMS AND METHODS TO PROCESS ELECTRONIC IMAGES TO CATEGORIZE INTRA-SLIDE SPECIMEN TISSUE TYPE
    4.
    发明申请
    SYSTEMS AND METHODS TO PROCESS ELECTRONIC IMAGES TO CATEGORIZE INTRA-SLIDE SPECIMEN TISSUE TYPE 有权

    公开(公告)号：US20220375071A1

    公开(公告)日：2022-11-24

    申请号：US17646513

    申请日：2021-12-30

    Applicant: PAIGE.AI, Inc.

    Inventor： Ran GODRICH ,  Christopher KANAN

    IPC: G06T7/00 ,  G06T7/194 ,  G06N3/04

    Abstract: Systems and methods are disclosed for identifying tissue specimen types present in digital whole slide images. In some aspects, tissue specimen types may be identified using unsupervised machine learning techniques for out-of-distribution detection. For example, a digital whole slide image of a tissue specimen and a recorded tissue specimen type for the digital whole slide image may be received. One or more feature vectors may be extracted from one or more foreground tiles of the digital whole slide image identified as including the tissue specimen, and a distribution learned by a machine learning system for the recorded tissue specimen type may be received. Using the distribution, a probability of the feature vectors corresponding to the recorded tissue specimen type may be computed and used as a basis for classifying the foreground tiles from which the feature vectors are extracted as an in-distribution foreground tile or an out-of-distribution foreground tile.

    SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES
    5.
    发明申请
    SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES 有权

    公开(公告)号：US20220328190A1

    公开(公告)日：2022-10-13

    申请号：US17809313

    申请日：2022-06-28

    Applicant: PAIGE.AI, Inc.

    Inventor： Ran GODRICH ,  Jillian SUE ,  Leo GRADY ,  Thomas FUCHS

    IPC: G16H50/20 ,  G16H70/60 ,  G16H40/20 ,  G16H10/40 ,  G16H30/40 ,  G16H70/20 ,  G16B40/20 ,  G06K9/62 ,  G06N20/00 ,  G06T7/00

    Abstract: An image processing method including identifying, using a machine learning system, an area of interest of a target image by analyzing features extracted from image regions in the target image, the machine learning system being generated by processing a plurality of training images each comprising an image of human tissue and a diagnostic label characterizing at least one of a slide morphology, a diagnostic value, and a pathologist review outcome; determining, using the machine learning system, a probability of a target feature being present in the area of interest of the target image based on an average probability; determining, using the machine learning system, a prioritization value, of a plurality of prioritization values, of the target image based on the probability of the target feature being present in the target image.

    SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES USING UNCERTAINTY ESTIMATION
    8.
    发明公开
    SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES USING UNCERTAINTY ESTIMATION 审中-公开

    公开(公告)号：US20230222653A1

    公开(公告)日：2023-07-13

    申请号：US18152305

    申请日：2023-01-10

    Applicant: PAIGE.AI, Inc.

    Inventor： Ran GODRICH ,  Christopher KANAN ,  Siqi LIU

    IPC: G06T7/00 ,  G16B20/00 ,  G16B40/00

    CPC classification number: G06T7/0012 ,  G16B20/00 ,  G16B40/00 ,  G06T2207/20081 ,  G06T2207/10056

    Abstract: A method for processing electronic images using uncertainty estimation may be used to determine whether to use an artificial intelligence (AI) assisted prediction. The method may include receiving one or more electronic images associated with a pathology specimen and providing the one or more electronic images to a machine learning model. The machine learning model may perform operations including determining a certainty level corresponding to a certainty that a predetermined AI system will provide an accurate prediction, determining whether the certainty level equals or exceeds a predetermined confidence threshold, and, upon determining that the certainty level does not equal or exceed a predetermined confidence threshold, determining to not use the predetermined AI system.

    SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES
    10.
    发明申请
    SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES 有权

    公开(公告)号：US20220076416A1

    公开(公告)日：2022-03-10

    申请号：US17530028

    申请日：2021-11-18

    Applicant: PAIGE.AI, Inc.

    Inventor： Ran GODRICH ,  Jillian SUE ,  Leo GRADY ,  Thomas FUCHS

    IPC: G06T7/00 ,  G06N20/00 ,  G16H50/20

    Abstract: An image processing method including identifying, using a machine learning system, an area of interest of a target image by analyzing microscopic features extracted from multiple image regions in the target image, the machine learning system being generated by processing a plurality of training images each comprising an image of human tissue and a diagnostic label characterizing at least one of a slide morphology, a diagnostic value, a pathologist review outcome, and an analytic difficulty; determining, using the machine learning system, a probability of a target feature being present in the area of interest of the target image based on an average probability; and determining, using the machine learning system, a prioritization value, of a plurality of prioritization values, of the target image based on the probability of the target feature being present in the target image.

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