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公开(公告)号：US11615534B2
公开(公告)日：2023-03-28
申请号：US17457268
申请日：2021-12-02
Applicant: PAIGE.AI, Inc.
Inventor： Jillian Sue , Razik Yousfi , Peter Schueffler , Thomas Fuchs , Leo Grady
Abstract: 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.
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32.发明授权公开(公告)号：US11423547B2
公开(公告)日：2022-08-23
申请号：US17480826
申请日：2021-09-21
Applicant: PAIGE.AI, Inc.
Inventor： Brandon Rothrock , Christopher Kanan , Julian Viret , Thomas Fuchs , Leo Grady
Abstract: 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.
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公开(公告)号：US20210210195A1
公开(公告)日：2021-07-08
申请号：US17126865
申请日：2020-12-18
Applicant: PAIGE.AI, Inc.
Inventor： Belma Dogdas , Christopher Kanan , Thomas Fuchs , Leo Grady
Abstract: 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.
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公开(公告)号：US20210209760A1
公开(公告)日：2021-07-08
申请号：US17126596
申请日：2020-12-18
Applicant: PAIGE.AI, Inc.
Inventor： Jillian SUE , Razik Yousfi , Peter Schueffler , Thomas Fuchs , Leo Grady
Abstract: 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.
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35.发明授权公开(公告)号：US12236365B2
公开(公告)日：2025-02-25
申请号：US18396868
申请日：2023-12-27
Applicant: PAIGE.AI, Inc.
Inventor： Supriya Kapur , Christopher Kanan , Thomas Fuchs , Leo Grady
Abstract: 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 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.
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Patent Agency Ranking
36.发明授权公开(公告)号：US11893510B2
公开(公告)日：2024-02-06
申请号：US18149969
申请日：2023-01-04
Applicant: PAIGE.AI, Inc.
Inventor： Supriya Kapur , Christopher Kanan , Thomas Fuchs , Leo Grady
CPC classification number: G06N5/04 , G06N20/00 , G06T7/0012 , G06T2207/20076 , G06T2207/20081 , G06T2207/30168
Abstract: 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 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.
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37.发明授权公开(公告)号：US11676704B2
公开(公告)日：2023-06-13
申请号：US17107121
申请日：2020-11-30
Applicant: PAIGE.AI, Inc.
Inventor： Christopher Kanan , Rodrigo Ceballos Lentini , Jillian Sue , Thomas Fuchs , Leo Grady
CPC classification number: G16H30/40 , G06T7/0012 , G16H50/20 , G06T2207/20081 , G06T2207/20084
Abstract: Systems and methods are disclosed for determining at least one geographic region of a plurality of geographic regions, at least one data variable, and/or at least one health variable, estimating a current prevalence of a data variable in a geographic region of the plurality of geographic regions, determining a trend in a relationship between the data variable and the geographic region at a current time, determining a second trend in the relationship between the data variable and the geographic region at at least one prior point in time, determining if the trend in the relationship is irregular within a predetermined threshold with respect to the second trend from the at least one prior point in time, and, upon determining that the trend in the relationship is irregular within a predetermined threshold, generating an alert.
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38.发明授权公开(公告)号：US11574140B2
公开(公告)日：2023-02-07
申请号：US17313617
申请日：2021-05-06
Applicant: PAIGE.AI, Inc.
Inventor： Jillian Sue , Thomas Fuchs , Christopher Kanan
Abstract: Systems and methods are disclosed for identifying a diagnostic feature of a digitized pathology image, including receiving one or more digitized images of a pathology specimen, and medical metadata comprising at least one of image metadata, specimen metadata, clinical information, and/or patient information, applying a machine learning model to predict a plurality of relevant diagnostic features based on medical metadata, the machine learning model having been developed using an archive of processed images and prospective patient data, and determining at least one relevant diagnostic feature of the relevant diagnostic features for output to a display.
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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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公开(公告)号：US11322246B2
公开(公告)日：2022-05-03
申请号：US17380595
申请日：2021-07-20
Applicant: PAIGE.AI, Inc.
Inventor： Belma Dogdas , Christopher Kanan , Thomas Fuchs , Leo Grady
Abstract: 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.
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