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公开(公告)号：US11790297B2
公开(公告)日：2023-10-17
申请号：US17573184
申请日：2022-01-11
Applicant: Enlitic, Inc.
Inventor： Kevin Lyman , Li Yao , Eric C. Poblenz , Jordan Prosky , Ben Covington , Anthony Upton , Lionel Lints
IPC: G16H50/20 , G06Q10/0631 , G16H10/60 , G16H30/40 , G16H15/00 , G06T5/00 , G06T5/50 , G06T7/00 , G06T11/00 , G06N5/04 , G16H30/20 , G06N20/00 , G06F9/54 , G06T7/187 , G06T7/11 , G06F3/0482 , G06T3/40 , A61B5/00 , G06F21/62 , G06Q20/14 , G16H40/20 , G06F3/0484 , G16H10/20 , G06N5/045 , G06T7/10 , G06T11/20 , G06F16/245 , G06T7/44 , G06N20/20 , H04L67/12 , H04L67/01 , G06V10/82 , G06F18/40 , G06F18/214 , G06F18/21 , G06F18/2115 , G06F18/2415 , G06V10/25 , G06V30/19 , G06V10/764 , G06V40/16 , G06V10/22 , G16H50/70 , G06T7/70 , G16H50/30 , A61B5/055 , A61B6/03 , A61B8/00 , A61B6/00 , G06Q50/22 , G06F40/295 , G06F18/24 , G06F18/2111 , G06V30/194
CPC classification number: G06Q10/06315 , A61B5/7264 , G06F3/0482 , G06F3/0484 , G06F9/542 , G06F16/245 , G06F18/214 , G06F18/217 , G06F18/2115 , G06F18/2415 , G06F18/41 , G06F21/6254 , G06N5/04 , G06N5/045 , G06N20/00 , G06N20/20 , G06Q20/14 , G06T3/40 , G06T5/002 , G06T5/008 , G06T5/50 , G06T7/0012 , G06T7/0014 , G06T7/10 , G06T7/11 , G06T7/187 , G06T7/44 , G06T7/97 , G06T11/001 , G06T11/006 , G06T11/206 , G06V10/225 , G06V10/25 , G06V10/764 , G06V10/82 , G06V30/19173 , G06V40/171 , G16H10/20 , G16H10/60 , G16H15/00 , G16H30/20 , G16H30/40 , G16H40/20 , G16H50/20 , H04L67/01 , H04L67/12 , A61B5/055 , A61B6/032 , A61B6/5217 , A61B8/4416 , G06F18/2111 , G06F18/24 , G06F40/295 , G06Q50/22 , G06T7/70 , G06T2200/24 , G06T2207/10048 , G06T2207/10081 , G06T2207/10088 , G06T2207/10116 , G06T2207/10132 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004 , G06T2207/30008 , G06T2207/30016 , G06T2207/30061 , G06V30/194 , G06V2201/03 , G16H50/30 , G16H50/70
Abstract: A model-assisted annotating system is operable to receive a first set of annotation data, corresponding to a broad type of annotation data output. A first training step is performed to train a computer vision model using the first set of annotation data. A second set of annotation data corresponding to the broad type of annotation data output is generated performing an inference function utilizing the computer vision model on medical scans. Additional annotation data further specifies the broad type of annotation data output is received. A second training step is performed to generate an updated computer vision model using set of additional annotation data. A third set of annotation data corresponding to the specified type of annotation data output is generated by performing an updated inference function utilizing the updated computer vision model on medical scans.
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公开(公告)号：US11626195B2
公开(公告)日：2023-04-11
申请号：US17447708
申请日：2021-09-15
Applicant: Enlitic, Inc.
Inventor： Kevin Lyman , Anthony Upton , Lionel Lints , Ben Covington
IPC: G16H10/60 , G16H30/40 , G16H15/00 , G06K9/62 , G06T5/00 , G06T5/50 , G06T7/00 , G06T11/00 , G06N5/04 , G16H30/20 , G06N20/00 , G06F9/54 , G06T7/187 , G06T7/11 , G06F3/0482 , G06T3/40 , A61B5/00 , G16H50/20 , G06F21/62 , G06Q20/14 , G16H40/20 , G06F3/0484 , G06Q10/0631 , G16H10/20 , G06N5/045 , G06T7/10 , G06T11/20 , G06F16/245 , G06T7/44 , G06N20/20 , H04L67/12 , G06V10/22 , H04L67/01 , G06V10/82 , G16H50/70 , G06T7/70 , G16H50/30 , A61B5/055 , A61B6/03 , A61B8/00 , A61B6/00 , G06Q50/22 , G06F40/295 , G06V30/194
Abstract: A method comprises displaying, via an interactive interface, a medical scan and a plurality of prompts of each prompt decision tree of a plurality of prompt decision trees in succession, beginning with automatically determined starting prompts of each prompt decision tree, in accordance with corresponding nodes of each prompt decision tree until a leaf node of each prompt decision tree is ultimately selected. Labeling data indicating the ultimately selected leaf node of each prompt decision tree is determined for the medical scan.
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公开(公告)号：US20200160979A1
公开(公告)日：2020-05-21
申请号：US16365787
申请日：2019-03-27
Applicant: Enlitic, Inc.
Inventor： Kevin Lyman , Li Yao , Eric C. Poblenz , Jordan Prosky , Ben Covington , Anthony Upton , Lionel Lints
Abstract: A model-assisted annotating system is operable to receive a first set of annotation data for a first set of medical scans from a set of client devices. A computer vision model is trained by utilizing first set of medical scans and the first set of annotation data. A second set of annotation data for a second set of medical scans is generated by utilizing the computer vision model. The second set of medical scans and the second set of annotation data is transmitted to the set of client devices, and a set of additional annotation data is received in response. An updated computer vision model is generated by utilizing the set of additional annotation data. A third set of annotation data is generated for a third set of medical scans by utilizing the updated computer vision model for transmission to the set of client devices for display.
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公开(公告)号：US11551795B2
公开(公告)日：2023-01-10
申请号：US17666813
申请日：2022-02-08
Applicant: Enlitic, Inc.
Inventor： Li Yao , Jordan Prosky , Eric C. Poblenz , Kevin Lyman , Lionel Lints , Ben Covington , Anthony Upton
IPC: G06T7/00 , G16H10/60 , G16H30/40 , G16H15/00 , G06K9/62 , G06T5/00 , G06T5/50 , G06T11/00 , G06N5/04 , G16H30/20 , G06N20/00 , G06F9/54 , G06T7/187 , G06T7/11 , G06F3/0482 , G06T3/40 , A61B5/00 , G16H50/20 , G06F21/62 , G06Q20/14 , G16H40/20 , G06F3/0484 , G06Q10/06 , G16H10/20 , G06T7/10 , G06T11/20 , G06F16/245 , G06T7/44 , G06N20/20 , H04L67/12 , G06V10/22 , H04L67/01 , G06V10/82 , G16H50/70 , G06T7/70 , G16H50/30 , A61B5/055 , A61B6/03 , A61B8/00 , A61B6/00 , G06Q50/22 , G06F40/295 , G06V30/194
Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.
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公开(公告)号：US11211153B2
公开(公告)日：2021-12-28
申请号：US16363019
申请日：2019-03-25
Applicant: Enlitic, Inc.
Inventor： Kevin Lyman , Anthony Upton , Lionel Lints , Ben Covington , Alexander Rhodes
IPC: G16H10/60 , G16H30/40 , G16H15/00 , G06N5/04 , G16H30/20 , G16H50/20 , H04L29/06 , G06K9/62 , G06T5/00 , G06T5/50 , G06T7/00 , G06T11/00 , G06N20/00 , G06F9/54 , G06T7/187 , G06T7/11 , G06F3/0482 , G06T3/40 , A61B5/00 , G06F21/62 , G06Q20/14 , G16H40/20 , G06F3/0484 , G06Q10/06 , G16H10/20 , G06T7/10 , G06T11/20 , G06F16/245 , G06T7/44 , G06N20/20 , G06K9/20 , H04L29/08 , G16H50/70 , G06T7/70 , G16H50/30 , A61B5/055 , A61B6/03 , A61B8/00 , G06K9/66 , A61B6/00 , G06Q50/22 , G06F40/295
Abstract: A medical scan labeling quality assurance system is operable to transmit a selected set of medical scans to a set of client devices associated with an expert user and a selected set of users. The client devices display medical scans are displayed to the expert user and the set of users, and a set of labeling data generated via user input to each client device is received from each client device. A set of performance score data is generated based on comparing each set of labeling data to a set of golden labeling data that was received from the client device of the expert user. The set of performance score data is used to update user profiles of the set of users, and is transmitted to the set of client devices for display to the set of users.
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Patent Agency Ranking
公开(公告)号：US11152089B2
公开(公告)日：2021-10-19
申请号：US16362959
申请日：2019-03-25
Applicant: Enlitic, Inc.
Inventor： Kevin Lyman , Anthony Upton , Lionel Lints , Ben Covington
IPC: G16H30/20 , G16H10/60 , H04L29/06 , G16H30/40 , G16H15/00 , G06K9/62 , G06T5/00 , G06T5/50 , G06T7/00 , G06T11/00 , G06N5/04 , G06N20/00 , G06F9/54 , G06T7/187 , G06T7/11 , G06F3/0482 , G06T3/40 , A61B5/00 , G16H50/20 , G06F21/62 , G06Q20/14 , G16H40/20 , G06F3/0484 , G06Q10/06 , G16H10/20 , G06T7/10 , G06T11/20 , G06F16/245 , G06T7/44 , G06N20/20 , G06K9/20 , H04L29/08 , G16H50/70 , G06T7/70 , G16H50/30 , A61B5/055 , A61B6/03 , A61B8/00 , G06K9/66 , A61B6/00 , G06Q50/22 , G06F40/295
Abstract: A medical scan hierarchical labeling system stores labeling application data that includes application operational instructions and a plurality of prompt decision trees. A medical scan and the labeling application data are sent to a client device for storage. The client device executes the application operational instructions of the labeling application data, causing the client device to display, via an interactive interface, the medical scan and a plurality of prompts of each prompt decision tree in succession, beginning with automatically determined starting prompts of each prompt decision tree, in accordance with corresponding nodes of each prompt decision tree until a leaf node of each prompt decision tree is ultimately selected. The client device transmits labeling data indicating the ultimately selected leaf node of each prompt decision tree. A medical scan entry of the medical scan in a medical scan database is populated based on the set of labels.
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公开(公告)号：US20200160968A1
公开(公告)日：2020-05-21
申请号：US16363019
申请日：2019-03-25
Applicant: Enlitic, Inc.
Inventor： Kevin Lyman , Anthony Upton , Lionel Lints , Ben Covington , Alexander Rhodes
Abstract: A medical scan labeling quality assurance system is operable to transmit a selected set of medical scans to a set of client devices associated with an expert user and a selected set of users. The client devices display medical scans are displayed to the expert user and the set of users, and a set of labeling data generated via user input to each client device is received from each client device. A set of performance score data is generated based on comparing each set of labeling data to a set of golden labeling data that was received from the client device of the expert user. The set of performance score data is used to update user profiles of the set of users, and is transmitted to the set of client devices for display to the set of users.
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公开(公告)号：US11257575B2
公开(公告)日：2022-02-22
申请号：US16365787
申请日：2019-03-27
Applicant: Enlitic, Inc.
Inventor： Kevin Lyman , Li Yao , Eric C. Poblenz , Jordan Prosky , Ben Covington , Anthony Upton , Lionel Lints
IPC: G16H50/20 , G16H10/60 , H04L67/01 , G16H30/40 , G16H15/00 , G06K9/62 , G06T5/00 , G06T5/50 , G06T7/00 , G06T11/00 , G06N5/04 , G16H30/20 , G06N20/00 , G06F9/54 , G06T7/187 , G06T7/11 , G06F3/0482 , G06T3/40 , A61B5/00 , G06F21/62 , G06Q20/14 , G16H40/20 , G06F3/0484 , G06Q10/06 , G16H10/20 , G06T7/10 , G06T11/20 , G06F16/245 , G06T7/44 , G06N20/20 , G06K9/20 , H04L67/12 , G16H50/70 , G06T7/70 , G16H50/30 , A61B5/055 , A61B6/03 , A61B8/00 , G06K9/66 , A61B6/00 , G06Q50/22 , G06F40/295
Abstract: A model-assisted annotating system is operable to receive a first set of annotation data for a first set of medical scans from a set of client devices. A computer vision model is trained by utilizing first set of medical scans and the first set of annotation data. A second set of annotation data for a second set of medical scans is generated by utilizing the computer vision model. The second set of medical scans and the second set of annotation data is transmitted to the set of client devices, and a set of additional annotation data is received in response. An updated computer vision model is generated by utilizing the set of additional annotation data. A third set of annotation data is generated for a third set of medical scans by utilizing the updated computer vision model for transmission to the set of client devices for display.
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公开(公告)号：US20210407634A1
公开(公告)日：2021-12-30
申请号：US17447708
申请日：2021-09-15
Applicant: Enlitic, Inc.
Inventor： Kevin Lyman , Anthony Upton , Lionel Lints , Ben Covington
IPC: G16H10/60 , H04L29/06 , G16H30/40 , G16H15/00 , G06K9/62 , G06T5/00 , G06T5/50 , G06T7/00 , G06T11/00 , G06N5/04 , G16H30/20 , G06N20/00 , G06F9/54 , G06T7/187 , G06T7/11 , G06F3/0482 , G06T3/40 , A61B5/00 , G16H50/20 , G06F21/62 , G06Q20/14 , G16H40/20 , G06F3/0484 , G06Q10/06 , G16H10/20 , G06T7/10 , G06T11/20 , G06F16/245 , G06T7/44 , G06N20/20 , G06K9/20 , H04L29/08
Abstract: A method comprises displaying, via an interactive interface, a medical scan and a plurality of prompts of each prompt decision tree of a plurality of prompt decision trees in succession, beginning with automatically determined starting prompts of each prompt decision tree, in accordance with corresponding nodes of each prompt decision tree until a leaf node of each prompt decision tree is ultimately selected. Labeling data indicating the ultimately selected leaf node of each prompt decision tree is determined for the medical scan.
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公开(公告)号：US20210082547A1
公开(公告)日：2021-03-18
申请号：US17022324
申请日：2020-09-16
Applicant: Enlitic, Inc.
Inventor： Li Yao , Jordan Prosky , Eric C. Poblenz , Kevin Lyman , Lionel Lints , Ben Covington , Anthony Upton
IPC: G16H10/60 , H04L29/06 , G16H30/40 , G16H15/00 , G06K9/62 , G06T5/00 , G06T5/50 , G06T7/00 , G06T11/00 , G06N5/04 , G16H30/20 , G06N20/00 , G06F9/54 , G06T7/187 , G06T7/11 , G06F3/0482 , G06T3/40 , A61B5/00 , G16H50/20 , G06F21/62 , G06Q20/14 , G16H40/20 , G06F3/0484 , G06Q10/06 , G16H10/20 , G06T7/10 , G06T11/20 , G06F16/245 , G06T7/44 , G06N20/20 , G06K9/20 , H04L29/08
Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.
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