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公开(公告)号:US12169915B2
公开(公告)日:2024-12-17
申请号:US17732857
申请日:2022-04-29
Applicant: PAIGE.AI, Inc.
Inventor: Rodrigo Ceballos Lentini , Christopher Kanan
Abstract: A computer-implemented method for processing electronic medical images, the method including receiving a plurality of electronic medical images of a medical specimen. Each of the plurality of electronic medical images may be divided into a plurality of tiles. A plurality of sets of matching tiles may be determined, the tiles within each set corresponding to a given region of a plurality of regions of the medical specimen. For each tile of the plurality of sets of matching tiles, a blur score may be determined corresponding to a level of image blur of the tile. For each set of matching tiles, a tile may be determined with the blur score indicating the lowest level of blur. A composite electronic medical image, comprising a plurality of tiles from each set of matching tiles with the blur score indicating the lowest level of blur, may be determined and provided for display.
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42.
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.
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公开(公告)号:US11823436B2
公开(公告)日:2023-11-21
申请号:US17710613
申请日:2022-03-31
Applicant: PAIGE.AI, Inc.
Inventor: Belma Dogdas , Christopher Kanan , Thomas Fuchs , Leo Grady
CPC classification number: G06V10/764 , G06T7/0012 , G06V10/82 , G06V20/698 , G16H30/40 , G16H50/20 , 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:US11791035B2
公开(公告)日:2023-10-17
申请号:US17539664
申请日:2021-12-01
Applicant: PAIGE.AI, Inc.
Inventor: Patricia Raciti , Christopher Kanan , Alican Bozkurt , Belma Dogdas
CPC classification number: G16H30/40 , G06N20/00 , G06T7/0014 , G16H50/20 , G16H70/60
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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45.
公开(公告)号:US11727564B2
公开(公告)日:2023-08-15
申请号:US17815671
申请日:2022-07-28
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 , G06F18/23213 , G06N20/00 , G06T7/11 , G06V10/763 , G06V30/19107 , G16B40/00 , G16H50/20 , 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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公开(公告)号:US11508066B2
公开(公告)日:2022-11-22
申请号:US17399422
申请日:2021-08-11
Applicant: PAIGE.AI, Inc.
Inventor: Christopher Kanan , Belma Dogdas , Patricia Raciti , Matthew Lee , Alican Bozkurt , Leo Grady , Thomas Fuchs , Jorge S. Reis-Filho
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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47.
公开(公告)号:US11481899B2
公开(公告)日:2022-10-25
申请号:US17547695
申请日:2021-12-10
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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公开(公告)号:US11475990B2
公开(公告)日:2022-10-18
申请号:US17160129
申请日:2021-01-27
Applicant: PAIGE.AI, Inc.
Inventor: Jillian Sue , Jason Locke , Peter Schueffler , Christopher Kanan , Thomas Fuchs , Leo Grady
Abstract: Systems and methods are disclosed for receiving one or more digital images associated with a tissue specimen, a related case, a patient, and/or a plurality of clinical information, determining one or more of a prediction, a recommendation, and/or a plurality of data for the one or more digital images using a machine learning system, the machine learning system having been trained using a plurality of training images, to predict a biomarker and a plurality of genomic panel elements, and determining, based on the prediction, the recommendation, and/or the plurality of data, whether to log an output and at least one visualization region as part of a case history within a clinical reporting system.
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49.
公开(公告)号:US11455753B1
公开(公告)日:2022-09-27
申请号:US17643036
申请日:2021-12-07
Applicant: PAIGE.AI, Inc.
Inventor: Navid Alemi , Christopher Kanan , Leo Grady
Abstract: Systems and methods are disclosed for adjusting attributes of whole slide images, including stains therein. A portion of a whole slide image comprised of a plurality of pixels in a first color space and including one or more stains may be received as input. Based on an identified stain type of the stain(s), a machine-learned transformation associated with the stain type may be retrieved and applied to convert an identified subset of the pixels from the first to a second color space specific to the identified stain type. One or more attributes of the stain(s) may be adjusted in the second color space to generate a stain-adjusted subset of pixels, which are then converted back to the first color space using an inverse of the machine-learned transformation. A stain-adjusted portion of the whole slide image including at least the stain-adjusted subset of pixels may be provided as output.
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公开(公告)号:US11335462B1
公开(公告)日:2022-05-17
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
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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