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1.发明申请公开(公告)号：US20250131565A1
公开(公告)日：2025-04-24
申请号：US19000906
申请日：2024-12-24
Applicant: PAIGE.AI, Inc.
Inventor： Patricia RACITI , Christopher KANAN , Alican BOZKURT , Belma DOGDAS
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.
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2.发明公开公开(公告)号：US20240046615A1
公开(公告)日：2024-02-08
申请号：US18488364
申请日：2023-10-17
Applicant: PAIGE.AI, Inc.
Inventor： Belma DOGDAS , Christopher KANAN , Thomas FUCHS , Leo GRADY
CPC classification number: G06V10/764 , G16H50/20 , G16H30/40 , G06T7/0012 , G06V10/82 , G06V20/698 , G06T2207/20081 , G06T2207/30024 , G06T2207/30096
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.
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公开(公告)号：US20230019631A1
公开(公告)日：2023-01-19
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
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.
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公开(公告)号：US20220130041A1
公开(公告)日：2022-04-28
申请号：US17519106
申请日：2021-11-04
Applicant: PAIGE.AI, Inc.
Inventor： Belma DOGDAS , Christopher KANAN , Thomas FUCHS , Leo GRADY
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.
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公开(公告)号：US20250131691A1
公开(公告)日：2025-04-24
申请号：US19001019
申请日：2024-12-24
Applicant: PAIGE.AI, Inc.
Inventor： Belma DOGDAS , Christopher KANAN , Thomas FUCHS , Leo GRADY
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.
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Patent Agency Ranking
6.发明公开公开(公告)号：US20230342931A1
公开(公告)日：2023-10-26
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 , G06T7/11 , G16H50/20 , G06N20/00 , G06V10/762 , G06V30/19 , G16B40/00 , G06F18/23213
CPC classification number: G06T7/0012 , G06T7/11 , G16H50/20 , G06N20/00 , G06V10/763 , G06V30/19107 , G16B40/00 , G06F18/23213 , 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.
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7.发明申请公开(公告)号：US20220366563A1
公开(公告)日：2022-11-17
申请号：US17815671
申请日：2022-07-28
Applicant: PAIGE.AI, Inc.
Inventor： Rodrigo CEBALLOS LENTINI , Christopher KANAN , Belma DOGDAS
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.
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8.发明申请公开(公告)号：US20210209753A1
公开(公告)日：2021-07-08
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
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.
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公开(公告)号：US20250078988A1
公开(公告)日：2025-03-06
申请号：US18948792
申请日：2024-11-15
Applicant: PAIGE.AI, Inc.
Inventor： Patricia RACITI , Christopher KANAN , Alican BOZKURT , Belma DOGDAS
Abstract: Systems and methods are disclosed for verifying slide and block quality for testing. The method may comprise receiving a collection of one or more digital images at a digital storage device. The collection may be associated with a tissue block and corresponding to an instance. The method may comprise applying a machine learning model to the collection to identify a presence or an absence of an attribute, determining an amount or a percentage of tissue with the attribute from a digital image in the collection that indicates the presence of the attribute, and outputting a quality score corresponding to the determined amount or percentage.
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公开(公告)号：US20240144477A1
公开(公告)日：2024-05-02
申请号：US18400016
申请日：2023-12-29
Applicant: PAIGE.AI, Inc.
Inventor： Christopher KANAN , Belma DOGDAS , Patricia RACITI , Matthew LEE , Alican BOZKURT , Leo GRADY , Thomas FUCHS , Jorge S. REIS-FILHO
CPC classification number: G06T7/0012 , G06F18/214 , G06N20/00 , G06T7/11 , G06V10/462 , G16H10/60 , G16H30/40 , G16H50/20 , G06T2207/10081 , G06T2207/10088 , G06T2207/10104 , G06T2207/20081 , G06T2207/20084 , G06T2207/30024 , G06T2207/30068 , G06V2201/03
Abstract: Systems and methods are disclosed for processing digital images to predict at least one continuous value comprising receiving one or more digital medical images, determining whether the one or more digital medical images includes at least one salient region, upon determining that the one or more digital medical images includes the at least one salient region, predicting, by a trained machine learning system, at least one continuous value corresponding to the at least one salient region, and outputting the at least one continuous value to an electronic storage device and/or display.
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