公开(公告)号：US11545253B2

公开(公告)日：2023-01-03

申请号：US17646500

申请日：2021-12-30

Applicant: PAIGE.AI, Inc.

Inventor： Ran Godrich ,  Christopher Kanan

IPC: G16H30/20 ,  G06N20/00 ,  G06T7/00 ,  G06T7/11

Abstract: Systems and methods are disclosed for identifying tissue specimen types present in digital whole slide images. In some aspects, tissue specimen types may be identified using unsupervised machine learning techniques for out-of-distribution detection. For example, a digital whole slide image of a tissue specimen and a recorded tissue specimen type for the digital whole slide image may be received. One or more feature vectors may be extracted from one or more foreground tiles of the digital whole slide image identified as including the tissue specimen, and a distribution learned by a machine learning system for the recorded tissue specimen type may be received. Using the distribution, a probability of the feature vectors corresponding to the recorded tissue specimen type may be computed and used as a basis for classifying the foreground tiles from which the feature vectors are extracted as an in-distribution foreground tile or an out-of-distribution foreground tile.

公开(公告)号：US11544849B2

公开(公告)日：2023-01-03

申请号：US17646513

申请日：2021-12-30

Applicant: PAIGE.AI, Inc.

Inventor： Ran Godrich ,  Christopher Kanan

IPC: G06T7/00 ,  G06T7/194 ,  G06N3/04 ,  G06T7/11

Abstract: Systems and methods are disclosed for identifying tissue specimen types present in digital whole slide images. In some aspects, tissue specimen types may be identified using unsupervised machine learning techniques for out-of-distribution detection. For example, a digital whole slide image of a tissue specimen and a recorded tissue specimen type for the digital whole slide image may be received. One or more feature vectors may be extracted from one or more foreground tiles of the digital whole slide image identified as including the tissue specimen, and a distribution learned by a machine learning system for the recorded tissue specimen type may be received. Using the distribution, a probability of the feature vectors corresponding to the recorded tissue specimen type may be computed and used as a basis for classifying the foreground tiles from which the feature vectors are extracted as an in-distribution foreground tile or an out-of-distribution foreground tile.

公开(公告)号：US11488719B2

公开(公告)日：2022-11-01

申请号：US17519834

申请日：2021-11-05

Applicant: PAIGE.AI, Inc.

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

IPC: G16H50/20 ,  G16H30/40 ,  G16H10/20 ,  G06V10/25 ,  G06V30/19 ,  G06K9/62 ,  G06T7/00

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：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

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：US11315249B2

公开(公告)日：2022-04-26

申请号：US17350328

申请日：2021-06-17

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

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：US11309074B2

公开(公告)日：2022-04-19

申请号：US17346923

申请日：2021-06-14

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：US11276499B1

公开(公告)日：2022-03-15

申请号：US17504867

申请日：2021-10-19

Applicant: PAIGE.AI, Inc.

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

IPC: G16H50/20 ,  G06K9/62 ,  G06T7/00 ,  G06K9/32 ,  G16H30/40 ,  G16H10/20

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：US11182899B2

公开(公告)日：2021-11-23

申请号：US17119767

申请日：2020-12-11

Applicant: PAIGE.AI, Inc.

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

IPC: G06T7/00 ,  G06T7/194 ,  G06T7/11 ,  G06K9/46 ,  G06K9/62

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：US11042807B2

公开(公告)日：2021-06-22

申请号：US17112435

申请日：2020-12-04

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, which may also be known as a machine learning system, 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：US10891550B2

公开(公告)日：2021-01-12

申请号：US16875616

申请日：2020-05-15

Applicant: PAIGE.AI, Inc.

Inventor： Supriya Kapur ,  Christopher Kanan ,  Thomas Fuchs ,  Leo Grady

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

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.