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    Systems and methods for processing electronic images
    2.
    发明授权
    Systems and methods for processing electronic images 有权

    公开(公告)号：US11210787B1

    公开(公告)日：2021-12-28

    申请号：US17377260

    申请日：2021-07-15

    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 receiving a target image of a slide corresponding to a target specimen comprising a tissue sample of a patient; generating a machine learning system by processing a plurality of training images, each training image comprising an image of human tissue and a label characterizing at least one of a slide morphology, a diagnostic value, a pathologist review outcome, and an analytic difficulty; automatically identifying, using the machine learning system, an area of interest of the target image by analyzing microscopic features extracted from multiple image regions in the target image; 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.

    Systems and methods for processing images of slides to infer biomarkers
    3.
    发明授权
    Systems and methods for processing images of slides to infer biomarkers 有权

    公开(公告)号：US12266096B2

    公开(公告)日：2025-04-01

    申请号：US17016048

    申请日：2020-09-09

    Applicant: PAIGE.AI, Inc.

    Inventor： Supriya Kapur ,  Ran Godrich ,  Christopher Kanan ,  Thomas Fuchs ,  Leo Grady

    IPC: G06T7/00 ,  G06F18/214 ,  G06T7/11 ,  G06V20/69 ,  G16H10/40 ,  G16H30/40 ,  G16H50/20

    Abstract: Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to identify a region of interest of the target specimen and determine an expression level of, category of, and/or presence of a biomarker in the region of interest, the biomarker comprising at least one from among an epithelial growth factor receptor (EGFR) biomarker and/or a DNA mismatch repair (MMR) deficiency biomarker, the machine learning system having been generated by processing a plurality of training images to predict whether a region of interest is present in the target electronic image, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the determined expression level of, category of, and/or presence of the biomarker in the region of interest.

    Systems and methods to process electronic images to categorize intra-slide specimen tissue type
    5.
    发明授权
    Systems and methods to process electronic images to categorize intra-slide specimen tissue type 有权

    公开(公告)号：US11545253B2

    公开(公告)日：2023-01-03

    申请号：US17646500

    申请日：2021-12-30

    Applicant: PAIGE.AI, Inc.

    Inventor： Ran Godrich ,  Christopher Kanan

    IPC: G16H30/20 ,  G06N20/00 ,  G06T7/00 ,  G06T7/11

    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 to process electronic images to categorize intra-slide specimen tissue type
    6.
    发明授权
    Systems and methods to process electronic images to categorize intra-slide specimen tissue type 有权

    公开(公告)号：US11544849B2

    公开(公告)日：2023-01-03

    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 ,  G06T7/11

    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
    7.
    发明授权
    Systems and methods for processing electronic images 有权

    公开(公告)号：US11456077B2

    公开(公告)日：2022-09-27

    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 ,  G16H70/60 ,  G16H40/20 ,  G16H10/40 ,  G16H30/40 ,  G16H70/20 ,  G16B40/20 ,  G06K9/62

    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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