公开(公告)号：US20230020368A1

公开(公告)日：2023-01-19

申请号：US17933156

申请日：2022-09-19

Applicant: PAIGE.AI, Inc.

Inventor： Patricia RACITI ,  Christopher KANAN ,  Alican BOZKURT ,  Belma DOGDAS

IPC: G06T7/00 ,  G06N3/04 ,  G16H30/20 ,  G06V10/77 ,  G06T11/00

Abstract: A computer-implemented method may include receiving a collection of unstained digital histopathology slide images at a storage device and running a trained machine learning model on one or more slide images of the collection to infer a presence or an absence of a salient feature. The trained machine learning model may have been trained by processing a second collection of unstained or stained digital histopathology slide images and at least one synoptic annotation for one or more unstained or stained digital histopathology slide images of the second collection. The computer-implemented method may further include determining at least one map from output of the trained machine learning model and providing an output from the trained machine learning model to the storage device.

公开(公告)号：US20220293249A1

公开(公告)日：2022-09-15

申请号：US17565629

申请日：2021-12-30

Applicant: PAIGE.AI, Inc.

Inventor： Patricia RACITI ,  Christopher KANAN ,  Alican BOZKURT ,  Belma DOGDAS

IPC: G16H30/40 ,  G06N20/00 ,  G16H70/60 ,  G06T7/00 ,  G16H50/20

Abstract: Systems and methods are disclosed for verifying slide and block quality for testing. The method may comprise receiving a collection of one or more digital images at a digital storage device. The collection may be associated with a tissue block and corresponding to an instance. The method may comprise applying a machine learning model to the collection to identify a presence or an absence of an attribute, determining an amount or a percentage of tissue with the attribute from a digital image in the collection that indicates the presence of the attribute, and outputting a quality score corresponding to the determined amount or percentage.

公开(公告)号：US20220293242A1

公开(公告)日：2022-09-15

申请号：US17457451

申请日：2021-12-03

Applicant: PAIGE.AI, INC.

Inventor： Patricia RACITI ,  Christopher KANAN ,  Alican BOZKURT ,  Belma DOGDAS

IPC: G16H30/20 ,  G06T7/00 ,  G06V10/77 ,  G06T11/00

Abstract: A computer-implemented method may include receiving a collection of unstained digital histopathology slide images at a storage device and running a trained machine learning model on one or more slide images of the collection to infer a presence or an absence of a salient feature. The trained machine learning model may have been trained by processing a second collection of unstained or stained digital histopathology slide images and at least one synoptic annotation for one or more unstained or stained digital histopathology slide images of the second collection. The computer-implemented method may further include determining at least one map from output of the trained machine learning model and providing an output from the trained machine learning model to the storage device.

公开(公告)号：US20220292670A1

公开(公告)日：2022-09-15

申请号：US17547695

申请日：2021-12-10

Applicant: PAIGE.AI, INC.

Inventor： Patricia RACITI ,  Christopher KANAN ,  Alican BOZKURT ,  Belma DOGDAS

IPC: G06T7/00 ,  G06N3/04

Abstract: A computer-implemented method may include receiving a collection of unstained digital histopathology slide images at a storage device and running a trained machine learning model on one or more slide images of the collection to infer a presence or an absence of a salient feature. The trained machine learning model may have been trained by processing a second collection of unstained or stained digital histopathology slide images and at least one synoptic annotation for one or more unstained or stained digital histopathology slide images of the second collection. The computer-implemented method may further include determining at least one map from output of the trained machine learning model and providing an output from the trained machine learning model to the storage device.

公开(公告)号：US20210233236A1

公开(公告)日：2021-07-29

申请号：US17160127

申请日：2021-01-27

Applicant: PAIGE.AI, Inc.

Inventor： Belma DOGDAS ,  Christopher KANAN ,  Thomas FUCHS ,  Leo GRADY

IPC: G06T7/00 ,  G16H10/40

Abstract: Systems and methods are disclosed for receiving digital images of a pathology specimen from a patient, the pathology specimen comprising tumor tissue, the one or more digital images being associated with data about a plurality of biomarkers in the tumor tissue and data about a surrounding invasive margin around the tumor tissue; identifying the tumor tissue and the surrounding invasive margin region to be analyzed for each of the one or more digital images; generating, using a machine learning model on the one or more digital images, at least one inference of a presence of the plurality of biomarkers in the tumor tissue and the surrounding invasive margin region; determining a spatial relationship of each of the plurality of biomarkers identified in the tumor tissue and the surrounding invasive margin region to themselves and to other cell types; and determining a prediction for a treatment outcome and/or at least one treatment recommendation for the patient.