公开(公告)号：US11308616B2

公开(公告)日：2022-04-19

申请号：US17391997

申请日：2021-08-02

Applicant: PAIGE.AI, Inc.

Inventor： Rodrigo Ceballos Lentini ,  Christopher Kanan ,  Belma Dogdas

IPC: G06T7/00 ,  G06N20/00 ,  G06T7/11 ,  G16H50/20 ,  G16B40/00 ,  G06K9/62

Abstract: Systems and methods are disclosed for grouping cells in a slide image that share a similar target, comprising receiving a digital pathology image corresponding to a tissue specimen, applying a trained machine learning system to the digital pathology image, the trained machine learning system being trained to predict at least one target difference across the tissue specimen, and determining, using the trained machine learning system, one or more predicted clusters, each of the predicted clusters corresponding to a subportion of the tissue specimen associated with a target.

公开(公告)号：US11182900B2

公开(公告)日：2021-11-23

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.

公开(公告)号：US11062801B2

公开(公告)日：2021-07-13

申请号：US17137769

申请日：2020-12-30

Applicant: PAIGE.AI, Inc.

Inventor： Rodrigo Ceballos Lentini ,  Christopher Kanan ,  Patricia Raciti ,  Leo Grady ,  Thomas Fuchs

IPC: G16H30/40 ,  G16H50/20 ,  G06K9/62 ,  G06T7/00

Abstract: Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared.

公开(公告)号：US10937541B2

公开(公告)日：2021-03-02

申请号：US16884978

申请日：2020-05-27

Applicant: PAIGE.AI, Inc.

Inventor： Rodrigo Ceballos Lentini ,  Christopher Kanan ,  Patricia Raciti ,  Leo Grady ,  Thomas Fuchs

IPC: G16H30/40 ,  G16H50/20 ,  G06T7/00 ,  G06K9/62

Abstract: Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared.

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

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

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.

公开(公告)号：US12131469B2

公开(公告)日：2024-10-29

申请号：US18342032

申请日：2023-06-27

Applicant: PAIGE.AI, Inc.

Inventor： Rodrigo Ceballos Lentini ,  Christopher Kanan ,  Belma Dogdas

IPC: G06T7/00 ,  G06F18/23213 ,  G06N20/00 ,  G06T7/11 ,  G06V10/762 ,  G06V30/19 ,  G16B40/00 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G06F18/23213 ,  G06N20/00 ,  G06T7/11 ,  G06V10/763 ,  G06V30/19107 ,  G16B40/00 ,  G16H50/20 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30096

Abstract: Systems and methods are disclosed for grouping cells in a slide image that share a similar target, comprising receiving a digital pathology image corresponding to a tissue specimen, applying a trained machine learning system to the digital pathology image, the trained machine learning system being trained to predict at least one target difference across the tissue specimen, and determining, using the trained machine learning system, one or more predicted clusters, each of the predicted clusters corresponding to a subportion of the tissue specimen associated with a target.

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

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

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.

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

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

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.

公开(公告)号：US11626201B2

公开(公告)日：2023-04-11

申请号：US17806519

申请日：2022-06-13

Applicant: PAIGE.AI, Inc.

Inventor： Rodrigo Ceballos Lentini ,  Christopher Kanan

IPC: G16H30/20 ,  G16H50/50 ,  G16H30/40 ,  G06T7/00 ,  G06N20/00

Abstract: Systems and methods are disclosed for generating synthetic medical images, including images presenting rare conditions or morphologies for which sufficient data may be unavailable. In one aspect, style transfer methods may be used. For example, a target medical image, a segmentation mask identifying style(s) to be transferred to area(s) of the target, and source medical image(s) including the style(s) may be received. Using the mask, the target may be divided into tile(s) corresponding to the area(s) and input to a trained machine learning system. For each tile, gradients associated with a content and style of the tile may be output by the system. Pixel(s) of at least one tile of the target may be altered based on the gradients to maintain content of the target while transferring the style(s) of the source(s) to the target. The synthetic medical image may be generated from the target based on the altering.

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

IPC: G16H30/20 ,  G16H50/20 ,  G06K9/62 ,  G06T7/00

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.