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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.
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公开(公告)号：US11222424B2
公开(公告)日：2022-01-11
申请号：US17126596
申请日：2020-12-18
Applicant: PAIGE.AI, Inc.
Inventor： Jillian Sue , Razik Yousfi , Peter Schueffler , Thomas Fuchs , Leo Grady
Abstract: Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, determining a quality control (QC) machine learning model to predict a quality designation based on one or more artifacts, providing the digital image as an input to the QC machine learning model, receiving the quality designation for the digital image as an output from the machine learning model, and outputting the quality designation of the digital image. A quality assurance (QA) machine learning model may predict a disease designation based on one or more biomarkers. The digital image may be provided to the QA model which may output a disease designation. An external designation may be compared to the disease designation and a comparison result may be output.
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公开(公告)号：US12266101B2
公开(公告)日：2025-04-01
申请号：US17729041
申请日：2022-04-26
Applicant: PAIGE.AI, Inc.
Inventor： Razik Yousfi , Peter Schueffler , Thomas Fresneau , Alexander Tsema
Abstract: A method may process an electronic image corresponding to a medical sample associated with a patient. The method may include receiving a selection of one or more artificial intelligence (AI) algorithms, receiving one or more whole slide images of a medical sample associated with a patient, performing a task on the whole slide images, using the one or more selected AI algorithms, the whole slide images being stored in a first container, the whole slide images being originated from a first user, the task comprising determining a characteristic of the medical sample in the whole slide images, based on the characteristic of the whole slide image, generating metadata associated with the whole slide image, and storing the metadata in a second container.
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公开(公告)号：US11791036B2
公开(公告)日：2023-10-17
申请号：US17805992
申请日：2022-06-08
Applicant: PAIGE.AI, Inc.
Inventor： Razik Yousfi , Peter Schueffler , Thomas Fresneau , Alexander Tsema
CPC classification number: G16H30/40 , G06F18/2413 , G06N20/00 , G06T7/0012 , G16H10/60 , G16H30/20
Abstract: Systems and methods are disclosed for using an integrated computing platform to view and transfer digital pathology slides using artificial intelligence, the method including receiving at least one whole slide image in a cloud computing environment located in a first geographic region, the whole slide image depicting a medical sample associated with a patient, the patient being located in the first geographic region; storing the received whole slide image in a first encrypted bucket; applying artificial intelligence to perform a classification of the at least one whole slide image, the classification comprising steps to determine whether portions of the medical sample depicted in the whole slide image are healthy or diseased; based on the classification of the at least one whole slide image, generating metadata associated with the whole slide image; and storing the metadata in a second encrypted bucket.
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公开(公告)号：US11983796B2
公开(公告)日：2024-05-14
申请号：US17880766
申请日：2022-08-04
Applicant: PAIGE.AI, Inc.
Inventor： Alexandre Kirszenberg , Razik Yousfi , Thomas Fresneau , Peter Schueffler
Abstract: A method for processing an electronic image including receiving, by a viewer, the electronic image and a FOV (field of view), wherein the FOV includes at least one coordinate, at least one dimension, and a magnification factor, loading, by the viewer, a plurality of tiles within the FOV, determining, by the viewer, a state of the plurality of tiles in a cache, and in response to determining that the state of the plurality of tiles in the cache is a fully loaded state, rendering, by the viewer, the plurality of tiles to a display.
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Patent Agency Ranking
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.
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公开(公告)号：US11416963B2
公开(公告)日：2022-08-16
申请号：US17398388
申请日：2021-08-10
Applicant: PAIGE.AI, Inc.
Inventor： Alexandre Kirszenberg , Razik Yousfi , Thomas Fresneau , Peter Schueffler
Abstract: A method for processing an electronic image including receiving, by a viewer, the electronic image and a FOV (field of view), wherein the FOV includes at least one coordinate, at least one dimension, and a magnification factor, loading, by the viewer, a plurality of tiles within the FOV, determining, by the viewer, a state of the plurality of tiles in a cache, and in response to determining that the state of the plurality of tiles in the cache is a fully loaded state, rendering, by the viewer, the plurality of tiles to a display.
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公开(公告)号：US11386989B2
公开(公告)日：2022-07-12
申请号：US17530372
申请日：2021-11-18
Applicant: PAIGE.AI, Inc.
Inventor： Razik Yousfi , Peter Schueffler , Thomas Fresneau , Alexander Tsema
Abstract: Systems and methods are disclosed for using an integrated computing platform to view and transfer digital pathology slides using artificial intelligence, the method including receiving at least one whole slide image in a cloud computing environment located in a first geographic region, the whole slide image depicting a medical sample associated with a patient, the patient being located in the first geographic region; storing the received whole slide image in a first encrypted bucket; applying artificial intelligence to perform a classification of the at least one whole slide image, the classification comprising steps to determine whether portions of the medical sample depicted in the whole slide image are healthy or diseased; based on the classification of the at least one whole slide image, generating metadata associated with the whole slide image; and storing the metadata in a second encrypted bucket.
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9.发明授权公开(公告)号：US12094118B2
公开(公告)日：2024-09-17
申请号：US18329024
申请日：2023-06-05
Applicant: PAIGE.AI, Inc.
Inventor： Danielle Gorton , Patricia Raciti , Jillian Sue , Razik Yousfi
IPC: G06K9/00 , G06T7/00 , G06T11/60 , G06V10/12 , G06V10/25 , G06V10/77 , G16H10/40 , G16H15/00 , G16H30/40 , G16H50/20 , G16H80/00
CPC classification number: G06T7/0014 , G06T11/60 , G06V10/12 , G06V10/25 , G06V10/7715 , G16H10/40 , G16H15/00 , G16H30/40 , G16H50/20 , G16H80/00 , G06T2207/10004 , G06T2207/30004 , G06T2207/30024 , G06V2201/03
Abstract: A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious; receiving a selection of a part of the plurality of parts; determining whether a plurality of slides associated with the part are suspicious or non-suspicious; determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.
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公开(公告)号：US11928820B2
公开(公告)日：2024-03-12
申请号：US18174284
申请日：2023-02-24
Applicant: PAIGE.AI, Inc.
Inventor： Jillian Sue , Razik Yousfi , Peter Schueffler , Thomas Fuchs , Leo Grady
CPC classification number: G06T7/0014 , G16H30/40 , G16H50/20 , G16H70/60 , G06T2207/10056 , G06T2207/20081 , G06T2207/30024 , G06T2207/30168
Abstract: Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, determining a quality control (QC) machine learning model to predict a quality designation based on one or more artifacts, providing the digital image as an input to the QC machine learning model, receiving the quality designation for the digital image as an output from the machine learning model, and outputting the quality designation of the digital image. A quality assurance (QA) machine learning model may predict a disease designation based on one or more biomarkers. The digital image may be provided to the QA model which may output a disease designation. An external designation may be compared to the disease designation and a comparison result may be output.
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