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公开(公告)号：US20210233251A1
公开(公告)日：2021-07-29
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.
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公开(公告)号：US20240062372A1
公开(公告)日：2024-02-22
申请号：US18451507
申请日：2023-08-17
Applicant: PAIGE.AI, INC.
Inventor： Jillian SUE , Marc GOLDFINGER , Brandon ROTHROCK , Matthew LEE
CPC classification number: G06T7/0012 , G06T5/50 , G06V10/462 , G16H30/20 , G16H30/40 , G06T2207/20221 , G06T2207/30024 , G06T2207/30068
Abstract: Systems and methods are described herein for processing electronic medical images to predict a biomarker's presence, including receiving one or more digital medical images, the one or more digital medical images being of at least one pathology specimen associated with a patient. A machine learning system may determine a biomarker expression level prediction for the one or more digital medical images. The biomarker expression level prediction may be based on a determined transcriptomic score and protein expression score for the one or more digital medical images. A slide overlay indicating a region of tissue on the one or more digital medical images that is most likely to contribute to the slide level biomarker expression prediction may be generated.
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公开(公告)号：US20220358650A1
公开(公告)日：2022-11-10
申请号：US17813651
申请日：2022-07-20
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.
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公开(公告)号：US20220343508A1
公开(公告)日：2022-10-27
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.
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公开(公告)号：US20250166397A1
公开(公告)日：2025-05-22
申请号：US19028824
申请日：2025-01-17
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.
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Patent Agency Ranking
6.发明公开公开(公告)号：US20230360414A1
公开(公告)日：2023-11-09
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
CPC classification number: G06V20/695 , G06T7/11 , G06N20/00 , G06V20/698 , G06T7/194 , G16H30/40 , G06T7/0012 , G06F18/2431 , 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.
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7.发明公开公开(公告)号：US20230245477A1
公开(公告)日：2023-08-03
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 , G06T7/136 , G06T7/194 , G06N20/00 , G06T7/0012 , G06F18/2155 , 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.
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公开(公告)号：US20220005201A1
公开(公告)日：2022-01-06
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.
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9.发明公开公开(公告)号：US20240273927A1
公开(公告)日：2024-08-15
申请号：US18642080
申请日：2024-04-22
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.
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公开(公告)号：US20240177838A1
公开(公告)日：2024-05-30
申请号：US18521903
申请日：2023-11-28
Applicant: PAIGE.AI, Inc.
Inventor： Siqi LIU , Eugene VORONTSOV , Alican BOZKURT , George SHAIKOVSKI , Michal ZELECHOWSKI , Adam CASSON , Jan BERNHARD , Sid SENTHILNATHAN , Matthew LEE , Ran GODRICH , Thomas FUCHS , Brandon ROTHROCK
CPC classification number: G16H30/40 , G06T7/0012 , G06T7/11 , G06T9/00 , G06V10/764 , G16H30/20 , G16H50/20 , G16H50/70
Abstract: Systems and methods for processing digital medical images to infer metadata from those images are disclosed. In some aspects, digital medical images may be processed to infer metadata by receiving a plurality of digital medical images, receiving a prompt, the prompt being a request for a specific type of metadata to be inferred from the plurality of digital medical images, determining, using a trained foundation model, at least one feature descriptor from the plurality of digital medical images based on the prompt, and providing for output the at least one feature descriptor for each of the plurality of digital medical images.
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