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

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

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.

公开(公告)号：US12217483B2

公开(公告)日：2025-02-04

申请号：US18488364

申请日：2023-10-17

Applicant: PAIGE.AI, Inc.

Inventor： Belma Dogdas ,  Christopher Kanan ,  Thomas Fuchs ,  Leo Grady

IPC: G06V10/764 ,  G06T7/00 ,  G06V10/82 ,  G06V20/69 ,  G16H30/40 ,  G16H50/20

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.

公开(公告)号：US12217420B2

公开(公告)日：2025-02-04

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 ,  G06N3/045 ,  G06T11/00 ,  G06V10/77 ,  G16H30/20

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：US12148532B2

公开(公告)日：2024-11-19

申请号：US18150491

申请日：2023-01-05

Applicant: PAIGE.AI, Inc.

Inventor： Jillian Sue ,  Thomas Fuchs ,  Christopher Kanan

IPC: G16H50/20 ,  G06F18/2113 ,  G06F18/214 ,  G06T7/00 ,  G16H30/20

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.

公开(公告)号：US12131473B2

公开(公告)日：2024-10-29

申请号：US18523098

申请日：2023-11-29

Applicant: PAIGE.AI, Inc.

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

IPC: G06T7/00 ,  G06F18/214 ,  G16H30/40 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G06F18/214 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06V2201/03

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：US11994665B2

公开(公告)日：2024-05-28

申请号：US17815034

申请日：2022-07-26

Applicant: PAIGE.AI, Inc.

Inventor： Sam Seymour ,  Todd Parker ,  Alican Bozkurt ,  Christopher Kanan ,  Jeremy Daniel Kunz

IPC: G02B21/36 ,  G06T7/70

CPC classification number: G02B21/365 ,  G02B21/368 ,  G06T7/70

Abstract: A computer-implemented method of reviewing digital pathology data may include receiving a digital pathology image into a digital storage device, the digital pathology image being associated with a patient, providing for display the digital pathology image on a display, pairing the digital pathology image with a physical token of the digital pathology image in an interactive system, receiving one or more commands from the interactive system, determining one or more manipulations or modifications to the displayed digital pathology image based on the one or more commands, and providing for display a modified digital pathology image on the display according to the determined one or more manipulations or modifications.

公开(公告)号：US11978560B2

公开(公告)日：2024-05-07

申请号：US17951421

申请日：2022-09-23

Applicant: PAIGE.AI, Inc.

Inventor： Leo Grady ,  Christopher Kanan ,  Jorge Sergio Reis-Filho ,  Belma Dogdas ,  Matthew Houliston

IPC: G16H50/20 ,  G06F18/214 ,  G06T7/00 ,  G06V10/25 ,  G06V30/19 ,  G16H10/20 ,  G16H30/40

CPC classification number: G16H50/20 ,  G06F18/214 ,  G06T7/0012 ,  G06V10/25 ,  G06V30/19147 ,  G16H10/20 ,  G16H30/40 ,  G06T2207/20081 ,  G06T2207/30004 ,  G06V2201/03

Abstract: Systems and methods are disclosed for processing digital images to identify diagnostic tests, the method comprising receiving one or more digital images associated with a pathology specimen, determining a plurality of diagnostic tests, applying a machine learning system to the one or more digital images to identify any prerequisite conditions for each of the plurality of diagnostic tests to be applicable, the machine learning system having been trained by processing a plurality of training images, identifying, using the machine learning system, applicable diagnostic tests of the plurality of diagnostic tests based on the one or more digital images and the prerequisite conditions, and outputting the applicable diagnostic tests to a digital storage device and/or display.

公开(公告)号：US11869185B2

公开(公告)日：2024-01-09

申请号：US17804123

申请日：2022-05-26

Applicant: PAIGE.AI, Inc.

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

IPC: G06T7/00 ,  G16H50/20 ,  G16H30/40 ,  G06F18/214

CPC classification number: G06T7/0012 ,  G06F18/214 ,  G16H30/40 ,  G16H50/20 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06V2201/03

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：US11823378B2

公开(公告)日：2023-11-21

申请号：US17107433

申请日：2020-11-30

Applicant: PAIGE.AI, Inc.

Inventor： Patricia Raciti ,  Christopher Kanan ,  Thomas Fuchs ,  Leo Grady

IPC: G06T7/194 ,  G06V10/764 ,  G06T7/00 ,  G06T7/11 ,  G06V10/776 ,  G06V10/82 ,  G06V10/98 ,  G06V20/69

CPC classification number: G06T7/0012 ,  G06T7/11 ,  G06T7/194 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06V10/993 ,  G06V20/69 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06V2201/03

Abstract: Systems and methods are disclosed for receiving one or more digital images associated with a tissue specimen, detecting one or more image regions from a background of the one or more digital images, determining a prediction, using a machine learning system, of whether at least one first image region of the one or more image regions comprises at least one external contaminant, the machine learning system having been trained using a plurality of training images to predict a presence of external contaminants and/or a location of any external contaminants present in the tissue specimen, and determining, based on the prediction of whether a first image region comprises an external contaminant, whether to process the image region using an processing algorithm.

公开(公告)号：US11494907B2

公开(公告)日：2022-11-08

申请号：US17123658

申请日：2020-12-16

Applicant: PAIGE.AI, Inc.

Inventor： Belma Dogdas ,  Christopher Kanan ,  Thomas Fuchs ,  Leo Grady ,  Kenan Turnacioglu

IPC: G06T7/00 ,  G06T7/11 ,  G06V20/69

Abstract: Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of tissue specimen, determining a detection machine learning model, the detection machine learning model being generated by processing a plurality of training images to output a cancer qualification and further a cancer quantification if the cancer qualification is an confirmed cancer qualification, providing the digital image as an input to the detection machine learning model, receiving one of a pathological complete response (pCR) cancer qualification or a confirmed cancer quantification as an output from the detection machine learning model, and outputting the pCR cancer qualification or the confirmed cancer quantification.