-
公开(公告)号：US11430116B2
公开(公告)日：2022-08-30
申请号：US17470901
申请日：2021-09-09
Applicant: PAIGE.AI, Inc.
Inventor： Antoine Sainson , Brandon Rothrock , Razik Yousfi , Patricia Raciti , Matthew Hanna , Christopher Kanan
Abstract: Systems and methods are disclosed for identifying formerly conjoined pieces of tissue in a specimen, comprising receiving one or more digital images associated with a pathology specimen, identifying a plurality of pieces of tissue by applying an instance segmentation system to the one or more digital images, the instance segmentation system having been generated by processing a plurality of training images, determining, using the instance segmentation system, a prediction of whether any of the plurality of pieces of tissue were formerly conjoined, and outputting at least one instance segmentation to a digital storage device and/or display, the instance segmentation comprising an indication of whether any of the plurality of pieces of tissue were formerly conjoined.
-
公开(公告)号：US11176676B2
公开(公告)日：2021-11-16
申请号：US17159849
申请日：2021-01-27
Applicant: PAIGE.AI, Inc.
Inventor： Brandon Rothrock , Christopher Kanan , Julian Viret , Thomas Fuchs , Leo Grady
Abstract: Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.
-
公开(公告)号：US12236694B2
公开(公告)日：2025-02-25
申请号：US18346391
申请日：2023-07-03
Applicant: PAIGE.AI, Inc.
Inventor： Brandon Rothrock , Jillian Sue , Matthew Houliston , Patricia Raciti , Leo Grady
Abstract: A method of using machine learning to output task-specific predictions 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：US11430117B2
公开(公告)日：2022-08-30
申请号：US17492745
申请日：2021-10-04
Applicant: PAIGE.AI, Inc.
Inventor： Antoine Sainson , Brandon Rothrock , Razik Yousfi , Patricia Raciti , Matthew Hanna , Christopher Kanan
Abstract: Systems and methods are disclosed for identifying formerly conjoined pieces of tissue in a specimen, comprising receiving one or more digital images associated with a pathology specimen, identifying a plurality of pieces of tissue by applying an instance segmentation system to the one or more digital images, the instance segmentation system having been generated by processing a plurality of training images, determining, using the instance segmentation system, a prediction of whether any of the plurality of pieces of tissue were formerly conjoined, and outputting at least one instance segmentation to a digital storage device and/or display, the instance segmentation comprising an indication of whether any of the plurality of pieces of tissue were formerly conjoined.
-
Patent Agency Ranking
公开(公告)号：US11995903B2
公开(公告)日：2024-05-28
申请号：US18186252
申请日：2023-03-20
Applicant: PAIGE.AI, Inc.
Inventor： Brandon Rothrock , Christopher Kanan , Julian Viret , Thomas Fuchs , Leo Grady
CPC classification number: G06V20/695 , G06F18/2155 , G06N20/00 , G06T7/0012 , G06T7/136 , G06T7/194 , G06V10/26 , G06V10/28 , G06V20/698 , G06T2207/20081 , G06T2207/30024
Abstract: Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.
-
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
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：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
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：US11640719B2
公开(公告)日：2023-05-02
申请号：US17811960
申请日：2022-07-12
Applicant: PAIGE.AI, Inc.
Inventor： Brandon Rothrock , Christopher Kanan , Julian Viret , Thomas Fuchs , Leo Grady
Abstract: Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.
-
公开(公告)号：US11423547B2
公开(公告)日：2022-08-23
申请号：US17480826
申请日：2021-09-21
Applicant: PAIGE.AI, Inc.
Inventor： Brandon Rothrock , Christopher Kanan , Julian Viret , Thomas Fuchs , Leo Grady
Abstract: Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.
-
-
-
-
-
-
-
-
Search Results
9
records
(took 0.011 seconds)
