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公开(公告)号：US20240145067A1
公开(公告)日：2024-05-02
申请号：US18400539
申请日：2023-12-29
申请人： PAIGE.AI, Inc.
摘要： Systems and methods are disclosed for generating synthetic medical images, including images presenting rare conditions or morphologies for which sufficient data may be unavailable. In one aspect, style transfer methods may be used. For example, a target medical image, a segmentation mask identifying style(s) to be transferred to area(s) of the target, and source medical image(s) including the style(s) may be received. Using the mask, the target may be divided into tile(s) corresponding to the area(s) and input to a trained machine learning system. For each tile, gradients associated with a content and style of the tile may be output by the system. Pixel(s) of at least one tile of the target may be altered based on the gradients to maintain content of the target while transferring the style(s) of the source(s) to the target. The synthetic medical image may be generated from the target based on the altering.
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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
申请人： PAIGE.AI, Inc.
发明人： Belma DOGDAS , Christopher KANAN , Thomas FUCHS , Leo GRADY
CPC分类号： G06V10/764 , G16H50/20 , G16H30/40 , G06T7/0012 , G06V10/82 , G06V20/698 , G06T2207/20081 , G06T2207/30024 , G06T2207/30096
摘要： 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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3.发明公开公开(公告)号：US20230401685A1
公开(公告)日：2023-12-14
申请号：US18330745
申请日：2023-06-07
申请人： PAIGE.AI, Inc.
CPC分类号： G06T7/0002 , G16H50/70 , G16H10/60 , G16H50/20 , G06T2207/20081 , G06T2207/30056 , A61B2017/00969
摘要： Systems and methods are described herein for processing electronic medical images to predict one or more donor recipients for a patient. For example, a digital medical image of the patient may be received, wherein the patient is in need of a transplant. A trained machine learning system may be determined. The digital medical image may be provided into the trained machine learning system, the trained machine learning system determining a patient embedding. Using the patient embedding, a subset of donor recipients may be determined. Based on the subset of donor recipients a recommendation of optimal donors may be determined.
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4.发明公开公开(公告)号：US20230268059A1
公开(公告)日：2023-08-24
申请号：US18310801
申请日：2023-05-02
申请人： PAIGE.AI, Inc.
CPC分类号： G16H30/40 , G06T7/0012 , G16H50/20 , G06T2207/20081 , G06T2207/20084
摘要： Systems and methods are disclosed for determining at least one geographic region of a plurality of geographic regions, at least one data variable, and/or at least one health variable, estimating a current prevalence of a data variable in a geographic region of the plurality of geographic regions, determining a trend in a relationship between the data variable and the geographic region at a current time, determining a second trend in the relationship between the data variable and the geographic region at at least one prior point in time, determining if the trend in the relationship is irregular within a predetermined threshold with respect to the second trend from the at least one prior point in time, and, upon determining that the trend in the relationship is irregular within a predetermined threshold, generating an alert.
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5.发明公开公开(公告)号：US20230144137A1
公开(公告)日：2023-05-11
申请号：US18149969
申请日：2023-01-04
申请人： PAIGE.AI, Inc.
发明人： Supriya KAPUR , Christopher KANAN , Thomas FUCHS , Leo GRADY
CPC分类号： G06N5/04 , G06N20/00 , G06T7/0012 , G06T2207/20076 , G06T2207/20081 , G06T2207/30168
摘要： Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.
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公开(公告)号：US20230115448A1
公开(公告)日：2023-04-13
申请号：US17954490
申请日：2022-09-28
申请人： PAIGE.AI, Inc.
发明人： Jillian SUE , Matthew LEE , Christopher KANAN
摘要： A method for processing electronic medical images may include receiving an initial whole slide image of a pathology specimen, receiving information about slide quality aspects to modify, and generating a synthetic whole slide image by applying a machine learning model to modify the received initial whole slide image according to the received information. The pathology specimen may be associated with a patient. The synthetic whole slide image may have a reduced quality as compared to the initial whole slide 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
申请人： PAIGE.AI, Inc.
摘要： 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：US20230010654A1
公开(公告)日：2023-01-12
申请号：US17732857
申请日：2022-04-29
申请人： PAIGE.AI, Inc.
摘要： 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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9.发明申请公开(公告)号：US20220366619A1
公开(公告)日：2022-11-17
申请号：US17814072
申请日：2022-07-21
申请人： PAIGE.AI, Inc.
发明人： Navid ALEMI , Christopher KANAN , Leo GRADY
摘要： 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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10.发明申请公开(公告)号：US20220199234A1
公开(公告)日：2022-06-23
申请号：US17654614
申请日：2022-03-14
申请人： PAIGE.AI, Inc.
摘要： Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared.
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