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公开(公告)号：US20240185989A1
公开(公告)日：2024-06-06
申请号：US18441532
申请日：2024-02-14
Applicant: GE Precision Healthcare LLC
Inventor： Travis R. Frosch , Sylvain Adam , Garry M. Whitley , Heather Mc Combs Chait , Steve M. Lawson
CPC classification number: G16H30/20 , G06T7/0012 , G16H10/60 , G16H30/40 , G16H40/67 , G16H70/00 , G06T2207/20084 , G06T2207/30004
Abstract: A computer-implemented system is provided that includes a plurality of endpoint databases employed to store medical images and metadata that describes attributes of the respective medical images. A management and orchestration platform periodically scans the endpoint databases to determine an index of the recently acquired medical images and associated metadata and updates to metadata associated with previously stored medical images. A search engine executes the search function to retrieve at least one of the recently acquired medical images and associated metadata and the recent updates to metadata associated with the previously stored medical images. The platform can also provide project management services and various collaborative, social networking type services. For example, the platform can facilitate collaborative review of medical image data, allowing users to rate and review the medical image data, annotate the medical image data, edit and augment the medical image data, and the like.
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公开(公告)号：US11935643B2
公开(公告)日：2024-03-19
申请号：US17102620
申请日：2020-11-24
Applicant: GE Precision Healthcare LLC
Inventor： Travis R. Frosch , Sylvain Adam , Garry M. Whitley , Heather McCombs Chait , Steve M. Lawson
CPC classification number: G16H30/20 , G06T7/0012 , G16H10/60 , G16H30/40 , G16H40/67 , G16H70/00 , G06T2207/20084 , G06T2207/30004
Abstract: A computer-implemented system is provided that includes a plurality of endpoint databases employed to store medical images and metadata that describes attributes of the respective medical images. A management and orchestration platform periodically scans the endpoint databases to determine an index of the recently acquired medical images and associated metadata and updates to metadata associated with previously stored medical images. A search engine executes the search function to retrieve at least one of the recently acquired medical images and associated metadata and the recent updates to metadata associated with the previously stored medical images. The platform can also provide project management services and various collaborative, social networking type services. For example, the platform can facilitate collaborative review of medical image data, allowing users to rate and review the medical image data, annotate the medical image data, edit and augment the medical image data, and the like.
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Patent Agency Ranking
3.发明申请公开(公告)号：US20220335328A1
公开(公告)日：2022-10-20
申请号：US17235026
申请日：2021-04-20
Applicant: GE Precision Healthcare LLC
Inventor： Travis R. Frosch , Anastasia Marie Van Dyke Dunn , Garry M. Whitley , Alvaro Molina , Weston R. Olmstead
Abstract: Systems and techniques that facilitate automated machine learning model feedback with data capture and synthetic data generation are provided. In various embodiments, a receiver component can receive electronic input identifying a deployed machine learning model. In various aspects, a listener component can retrieve from a data pipeline a data candidate that has been analyzed by the deployed machine learning model, an inference generated by the deployed machine learning model based on the data candidate, and an expert conclusion provided by a subject matter expert based on the data candidate. In various instances, a comparison component can compare the inference with the expert conclusion to determine whether the inference is consistent with the expert conclusion. In various cases, an augmentation component can, in response to a determination that the inference is not consistent with the expert conclusion, generate a set of synthetic training data based on the data candidate.
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公开(公告)号：US20210035015A1
公开(公告)日：2021-02-04
申请号：US16528121
申请日：2019-07-31
Applicant: GE Precision Healthcare LLC
Inventor： Marc T. Edgar , Travis R. Frosch , Gopal B. Avinash , Garry M. Whitley
Abstract: Techniques are provided for enhancing the efficiency and accuracy of annotating data samples for supervised machine learning algorithms using an advanced annotation pipeline. According to an embodiment, a method can comprise collecting, by a system comprising a processor, unannotated data samples for input to a machine learning model and storing the unannotated data samples in an annotation queue. The method further comprises determining, by the system, annotation priority levels for respective unannotated data samples of the unannotated data samples, selecting, by the system from amongst different annotation techniques, one or more of the different annotation techniques for annotating the respective unannotated data samples based the annotation priority levels associated with the respective unannotated data samples.
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公开(公告)号：US11475358B2
公开(公告)日：2022-10-18
申请号：US16527965
申请日：2019-07-31
Applicant: GE Precision Healthcare LLC
Inventor： Marc T. Edgar , Travis R. Frosch , Gopal B. Avinash , Garry M. Whitley
IPC: G06N20/00 , G06N5/04 , G16H50/20 , G06F40/169 , G06K9/62
Abstract: Techniques are provided for enhancing the efficiency and accuracy of annotating data samples for supervised machine learning algorithms using an advanced annotation pipeline. According to an embodiment, a method can comprise collecting, by a system comprising a processor, unannotated data samples for input to a machine learning model and storing the unannotated data samples in an annotation queue. The method further comprises determining, by the system, annotation priority levels for respective unannotated data samples of the unannotated data samples, selecting, by the system from amongst different annotation techniques, one or more of the different annotation techniques for annotating the respective unannotated data samples based the annotation priority levels associated with the respective unannotated data samples.
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公开(公告)号：US20210158933A1
公开(公告)日：2021-05-27
申请号：US17102620
申请日：2020-11-24
Applicant: GE Precision Healthcare LLC
Inventor： Travis R. Frosch , Sylvain Adam , Garry M. Whitley , Heather Mc Combs Chait , Steve M. Lawson
Abstract: A computer-implemented system is provided that includes a plurality of endpoint databases employed to store medical images and metadata that describes attributes of the respective medical images. A management and orchestration platform periodically scans the endpoint databases to determine an index of the recently acquired medical images and associated metadata and updates to metadata associated with previously stored medical images. A search engine executes the search function to retrieve at least one of the recently acquired medical images and associated metadata and the recent updates to metadata associated with the previously stored medical images. The platform can also provide project management services and various collaborative, social networking type services. For example, the platform can facilitate collaborative review of medical image data, allowing users to rate and review the medical image data, annotate the medical image data, edit and augment the medical image data, and the like.
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公开(公告)号：US20210034920A1
公开(公告)日：2021-02-04
申请号：US16527965
申请日：2019-07-31
Applicant: GE Precision Healthcare LLC
Inventor： Marc T. Edgar , Travis R. Frosch , Gopal B. Avinash , Garry M. Whitley
Abstract: Techniques are provided for enhancing the efficiency and accuracy of annotating data samples for supervised machine learning algorithms using an advanced annotation pipeline. According to an embodiment, a method can comprise collecting, by a system comprising a processor, unannotated data samples for input to a machine learning model and storing the unannotated data samples in an annotation queue. The method further comprises determining, by the system, annotation priority levels for respective unannotated data samples of the unannotated data samples, selecting, by the system from amongst different annotation techniques, one or more of the different annotation techniques for annotating the respective unannotated data samples based the annotation priority levels associated with the respective unannotated data samples.
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