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48 条记录 (耗时 0.106 秒)

    Systems and methods for processing images to classify the processed images for digital pathology
    3.
    发明授权
    Systems and methods for processing images to classify the processed images for digital pathology 有权

    公开(公告)号：US11593684B2

    公开(公告)日：2023-02-28

    申请号：US17705908

    申请日：2022-03-28

    申请人： PAIGE.AI, Inc.

    发明人： Supriya Kapur ,  Christopher Kanan ,  Thomas Fuchs ,  Leo Grady

    IPC分类号： G06N5/04 ,  G06N20/00 ,  G06T7/00

    摘要： Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model, which may also be known as a machine learning system, to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.

    Systems and methods to process electronic images to adjust attributes of the electronic images
    4.
    发明授权
    Systems and methods to process electronic images to adjust attributes of the electronic images 有权

    公开(公告)号：US11455724B1

    公开(公告)日：2022-09-27

    申请号：US17457962

    申请日：2021-12-07

    申请人： PAIGE.AI, Inc.

    发明人： Navid Alemi ,  Christopher Kanan ,  Leo Grady

    IPC分类号： G06T7/00 ,  G06T7/90 ,  G06T5/00 ,  G06T3/40

    摘要： Systems and methods are disclosed for adjusting attributes of whole slide images, including stains therein. A portion of a whole slide image comprised of a plurality of pixels in a first color space and including one or more stains may be received as input. Based on an identified stain type of the stain(s), a machine-learned transformation associated with the stain type may be retrieved and applied to convert an identified subset of the pixels from the first to a second color space specific to the identified stain type. One or more attributes of the stain(s) may be adjusted in the second color space to generate a stain-adjusted subset of pixels, which are then converted back to the first color space using an inverse of the machine-learned transformation. A stain-adjusted portion of the whole slide image including at least the stain-adjusted subset of pixels may be provided as output.

    Systems and methods for processing images to classify the processed images for digital pathology
    5.
    发明授权
    Systems and methods for processing images to classify the processed images for digital pathology 有权

    公开(公告)号：US11315029B2

    公开(公告)日：2022-04-26

    申请号：US17303164

    申请日：2021-05-21

    申请人： PAIGE.AI, Inc.

    发明人： Supriya Kapur ,  Christopher Kanan ,  Thomas Fuchs ,  Leo Grady

    IPC分类号： G06N5/04 ,  G06T7/00 ,  G06N20/00

    摘要： Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model, which may also be known as a machine learning system, to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.

    Systems and methods for analyzing electronic images for quality control
    6.
    发明授权
    Systems and methods for analyzing electronic images for quality control 有权

    公开(公告)号：US11222424B2

    公开(公告)日：2022-01-11

    申请号：US17126596

    申请日：2020-12-18

    申请人： PAIGE.AI, Inc.

    发明人： Jillian Sue ,  Razik Yousfi ,  Peter Schueffler ,  Thomas Fuchs ,  Leo Grady

    IPC分类号： G06K9/00 ,  G06T7/00 ,  G16H70/60 ,  G16H50/20 ,  G16H30/40

    摘要： Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, determining a quality control (QC) machine learning model to predict a quality designation based on one or more artifacts, providing the digital image as an input to the QC machine learning model, receiving the quality designation for the digital image as an output from the machine learning model, and outputting the quality designation of the digital image. A quality assurance (QA) machine learning model may predict a disease designation based on one or more biomarkers. The digital image may be provided to the QA model which may output a disease designation. An external designation may be compared to the disease designation and a comparison result may be output.

    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：US11210787B1

    公开(公告)日：2021-12-28

    申请号：US17377260

    申请日：2021-07-15

    申请人： PAIGE.AI, Inc.

    发明人： Ran Godrich ,  Jillian Sue ,  Leo Grady ,  Thomas Fuchs

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

    摘要： 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 electronic images for computational detection methods
    8.
    发明授权
    Systems and methods for processing electronic images for computational detection methods 有权

    公开(公告)号：US11176676B2

    公开(公告)日：2021-11-16

    申请号：US17159849

    申请日：2021-01-27

    申请人： PAIGE.AI, Inc.

    发明人： Brandon Rothrock ,  Christopher Kanan ,  Julian Viret ,  Thomas Fuchs ,  Leo Grady

    IPC分类号： G06T7/136 ,  G06T7/00 ,  G06N20/00 ,  G06K9/62 ,  G06T7/194

    摘要： Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.

    Systems and methods for processing electronic images for generalized disease detection
    9.
    发明授权
    Systems and methods for processing electronic images for generalized disease detection 有权

    公开(公告)号：US11107573B2

    公开(公告)日：2021-08-31

    申请号：US17126865

    申请日：2020-12-18

    申请人： PAIGE.AI, Inc.

    发明人： Belma Dogdas ,  Christopher Kanan ,  Thomas Fuchs ,  Leo Grady

    IPC分类号： G16H30/40 ,  G16H50/20 ,  G06T7/00

    摘要： Systems and methods are disclosed for generating a specialized machine learning model by receiving a generalized machine learning model generated by processing a plurality of first training images to predict at least one cancer characteristic, receiving a plurality of second training images, the first training images and the second training images include images of tissue specimens and/or images algorithmically generated to replicate tissue specimens, receiving a plurality of target specialized attributes related to a respective second training image of the plurality of second training images, generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of second training images and the target specialized attributes, receiving a target image corresponding to a target specimen, applying the specialized machine learning model to the target image to determine at least one characteristic of the target image, and outputting the characteristic of the target image.