公开(公告)号：US12094118B2

公开(公告)日：2024-09-17

申请号：US18329024

申请日：2023-06-05

Applicant: PAIGE.AI, Inc.

Inventor： Danielle Gorton ,  Patricia Raciti ,  Jillian Sue ,  Razik Yousfi

IPC: G06K9/00 ,  G06T7/00 ,  G06T11/60 ,  G06V10/12 ,  G06V10/25 ,  G06V10/77 ,  G16H10/40 ,  G16H15/00 ,  G16H30/40 ,  G16H50/20 ,  G16H80/00

CPC classification number: G06T7/0014 ,  G06T11/60 ,  G06V10/12 ,  G06V10/25 ,  G06V10/7715 ,  G16H10/40 ,  G16H15/00 ,  G16H30/40 ,  G16H50/20 ,  G16H80/00 ,  G06T2207/10004 ,  G06T2207/30004 ,  G06T2207/30024 ,  G06V2201/03

Abstract: A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.

公开(公告)号：US11928820B2

公开(公告)日：2024-03-12

申请号：US18174284

申请日：2023-02-24

Applicant: PAIGE.AI, Inc.

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

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

CPC classification number: G06T7/0014 ,  G16H30/40 ,  G16H50/20 ,  G16H70/60 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30168

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.

公开(公告)号：US11663838B2

公开(公告)日：2023-05-30

申请号：US17511871

申请日：2021-10-27

Applicant: PAIGE.AI, Inc.

Inventor： Brandon Rothrock ,  Jillian Sue ,  Matthew Houliston ,  Patricia Raciti ,  Leo Grady

IPC: G06V20/69 ,  G06T7/11 ,  G06N20/00 ,  G06T7/194 ,  G16H30/40 ,  G06T7/00 ,  G06F18/2431

CPC classification number: G06V20/695 ,  G06F18/2431 ,  G06N20/00 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/194 ,  G06V20/698 ,  G16H30/40 ,  G06T2207/20081 ,  G06T2207/30024

Abstract: A method of using a machine learning model to output a task-specific prediction may include receiving a digitized cytology image of a cytology sample and applying a machine learning model to isolate cells of the digitized cytology image. The machine learning model may include identifying a plurality of sub-portions of the digitized cytology image, identifying, for each sub-portion of the plurality of sub-portions, either background or cell, and determining cell sub-images of the digitized cytology image. Each cell sub-image may comprise a cell of the digitized cytology image, based on the identifying either background or cell. The method may further comprise determining a plurality of features based on the cell sub-images, each of the cell sub-images being associated with at least one of the plurality of features, determining an aggregated feature based on the plurality of features, and training a machine learning model to predict a target task based on the aggregated feature.

公开(公告)号：US11538162B2

公开(公告)日：2022-12-27

申请号：US17565681

申请日：2021-12-30

Applicant: PAIGE.AI, Inc.

Inventor： Danielle Gorton ,  Patricia Raciti ,  Jillian Sue ,  Razik Yousfi

IPC: G06T7/00 ,  G06V10/25 ,  G06V10/12 ,  G06V10/77 ,  G16H10/40 ,  G16H15/00 ,  G16H50/20 ,  G16H30/40 ,  G16H80/00 ,  G06T11/60

Abstract: A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.

公开(公告)号：US11475990B2

公开(公告)日：2022-10-18

申请号：US17160129

申请日：2021-01-27

Applicant: PAIGE.AI, Inc.

Inventor： Jillian Sue ,  Jason Locke ,  Peter Schueffler ,  Christopher Kanan ,  Thomas Fuchs ,  Leo Grady

IPC: G16H30/20 ,  G06N20/00

Abstract: Systems and methods are disclosed for receiving one or more digital images associated with a tissue specimen, a related case, a patient, and/or a plurality of clinical information, determining one or more of a prediction, a recommendation, and/or a plurality of data for the one or more digital images using a machine learning system, the machine learning system having been trained using a plurality of training images, to predict a biomarker and a plurality of genomic panel elements, and determining, based on the prediction, the recommendation, and/or the plurality of data, whether to log an output and at least one visualization region as part of a case history within a clinical reporting system.

公开(公告)号：US12148159B2

公开(公告)日：2024-11-19

申请号：US18530844

申请日：2023-12-06

Applicant: PAIGE.AI, Inc.

Inventor： Jason Locke ,  Jillian Sue ,  Christopher Kanan ,  Sese Ih

IPC: G06K9/00 ,  A61B6/00 ,  G06N20/00 ,  G06T3/40 ,  G06T7/00 ,  G06T11/60 ,  G16H10/40 ,  G16H30/40 ,  G16H70/60

Abstract: Systems and methods are disclosed for analyzing an image of a slide corresponding to a specimen, the method including receiving at least one digitized image of a pathology specimen; determining, using the digitized image at an artificial intelligence (AI) system, at least one salient feature, the at least one salient comprising a biomarker, cancer, cancer grade, parasite, toxicity, inflammation, and/or cancer sub-type; determining, at the AI system, a salient region overlay for the digitized image, wherein the AI system indicates a value for each pixel; and suppressing, based on the value for each pixel, one or more non-salient regions of the digitized image.

公开(公告)号：US11887304B2

公开(公告)日：2024-01-30

申请号：US17655524

申请日：2022-03-18

Applicant: PAIGE.AI, Inc.

Inventor： Jason Locke ,  Jillian Sue ,  Christopher Kanan ,  Sese Ih

IPC: G06K9/00 ,  A61B6/00 ,  G06T7/00 ,  G16H70/60 ,  G16H10/40 ,  G16H30/40 ,  G06N20/00 ,  G06T3/40 ,  G06T11/60

CPC classification number: G06T7/0012 ,  G06N20/00 ,  G06T3/40 ,  G06T11/60 ,  G16H10/40 ,  G16H30/40 ,  G16H70/60 ,  G06T2207/20081 ,  G06T2207/30024

Abstract: Systems and methods are disclosed for analyzing an image of a slide corresponding to a specimen, the method including receiving at least one digitized image of a pathology specimen; determining, using the digitized image at an artificial intelligence (AI) system, at least one salient feature, the at least one salient comprising a biomarker, cancer, cancer grade, parasite, toxicity, inflammation, and/or cancer sub-type; determining, at the AI system, a salient region overlay for the digitized image, wherein the AI system indicates a value for each pixel; and suppressing, based on the value for each pixel, one or more non-salient regions of the digitized image.

公开(公告)号：US11776681B2

公开(公告)日：2023-10-03

申请号：US17809313

申请日：2022-06-28

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 ,  G06F18/214

CPC classification number: G16H30/40 ,  G06F18/214 ,  G06N20/00 ,  G06T7/0012 ,  G16B40/20 ,  G16H10/40 ,  G16H40/20 ,  G16H50/20 ,  G16H70/20 ,  G16H70/60 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06T2207/30204 ,  G06V2201/03 ,  G06V2201/04

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.

公开(公告)号：US11721115B2

公开(公告)日：2023-08-08

申请号：US17519847

申请日：2021-11-05

Applicant: PAIGE.AI, Inc.

Inventor： Brandon Rothrock ,  Jillian Sue ,  Matthew Houliston ,  Patricia Raciti ,  Leo Grady

IPC: G06V20/69 ,  G06T7/11 ,  G06K9/62 ,  G06N20/00 ,  G06T7/194 ,  G16H30/40 ,  G06T7/00 ,  G06F18/2431

CPC classification number: G06V20/695 ,  G06F18/2431 ,  G06N20/00 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/194 ,  G06V20/698 ,  G16H30/40 ,  G06T2207/20081 ,  G06T2207/30024

Abstract: A method of using a machine learning model to output a task-specific prediction may include receiving a digitized cytology image of a cytology sample and applying a machine learning model to isolate cells of the digitized cytology image. The machine learning model may include identifying a plurality of sub-portions of the digitized cytology image, identifying, for each sub-portion of the plurality of sub-portions, either background or cell, and determining cell sub-images of the digitized cytology image. Each cell sub-image may comprise a cell of the digitized cytology image, based on the identifying either background or cell. The method may further comprise determining a plurality of features based on the cell sub-images, each of the cell sub-images being associated with at least one of the plurality of features, determining an aggregated feature based on the plurality of features, and training a machine learning model to predict a target task based on the aggregated feature.

公开(公告)号：US11710235B2

公开(公告)日：2023-07-25

申请号：US17552438

申请日：2021-12-16

Applicant: PAIGE.AI, Inc.

Inventor： Danielle Gorton ,  Patricia Raciti ,  Jillian Sue ,  Razik Yousfi

IPC: G06K9/00 ,  G06T7/00 ,  G06V10/25 ,  G06V10/12 ,  G06V10/77 ,  G16H10/40 ,  G16H15/00 ,  G16H50/20 ,  G16H30/40 ,  G16H80/00 ,  G06T11/60

CPC classification number: G06T7/0014 ,  G06T11/60 ,  G06V10/12 ,  G06V10/25 ,  G06V10/7715 ,  G16H10/40 ,  G16H15/00 ,  G16H30/40 ,  G16H50/20 ,  G16H80/00 ,  G06T2207/10004 ,  G06T2207/30004 ,  G06T2207/30024 ,  G06V2201/03

Abstract: A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.