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公开(公告)号：US11954610B2
公开(公告)日：2024-04-09
申请号：US16944762
申请日：2020-07-31
Applicant: GE Precision Healthcare LLC
Inventor： Junpyo Hong , Venkata Ratnam Saripalli , Gopal B. Avinash , Karley Marty Yoder , Keith Bigelow
Abstract: Techniques are described for performing active surveillance and learning for machine learning (ML) model authoring and deployment workflows. In an embodiment, a method comprises applying, by a system comprising a processor, a primary ML model trained on a training dataset to data samples excluded from the training dataset to generate inferences based on the data samples. The method further comprises employing, by the system, one or more active surveillance techniques to regulate performance of the primary ML model in association with the applying, wherein the one or more active surveillance techniques comprise at least one of, performing a model scope evaluation of the primary ML model relative to the data samples or using a domain adapted version of the primary ML model to generate the inferences.
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公开(公告)号：US20210042643A1
公开(公告)日：2021-02-11
申请号：US16944762
申请日：2020-07-31
Applicant: GE Precision Healthcare LLC
Inventor： Junpyo Hong , Venkata Ratnam Saripalli , Gopal B. Avinash , Karley Marty Yoder , Keith Bigelow
Abstract: Techniques are described for performing active surveillance and learning for machine learning (ML) model authoring and deployment workflows. In an embodiment, a method comprises applying, by a system comprising a processor, a primary ML model trained on a training dataset to data samples excluded from the training dataset to generate inferences based on the data samples. The method further comprises employing, by the system, one or more active surveillance techniques to regulate performance of the primary ML model in association with the applying, wherein the one or more active surveillance techniques comprise at least one of, performing a model scope evaluation of the primary ML model relative to the data samples or using a domain adapted version of the primary ML model to generate the inferences.
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公开(公告)号：US12159420B2
公开(公告)日：2024-12-03
申请号：US17457179
申请日：2021-12-01
Applicant: GE Precision Healthcare LLC
Inventor： Dibyajyoti Pati , Junpyo Hong , Venkata Ratnam Saripalli , German Guillermo Vera Gonzalez , Dejun Wang , Aizhen Zhou , Gopal B. Avinash , Ravi Soni , Tao Tan , Fuqiang Chen , Yaan Ge
Abstract: Various methods and systems are provided for automatically registering and stitching images. In one example, a method includes entering a first image of a subject and a second image of the subject to a model trained to output a transformation matrix based on the first image and the second image, where the model is trained with a plurality of training data sets, each training data set including a pair of images, a mask indicating a region of interest (ROI), and associated ground truth, automatically stitching together the first image and the second image based on the transformation matrix to form a stitched image, and outputting the stitched image for display on a display device and/or storing the stitched image in memory.
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Patent Agency Ranking
公开(公告)号：US20220253708A1
公开(公告)日：2022-08-11
申请号：US17174049
申请日：2021-02-11
Applicant: GE Precision Healthcare LLC
Inventor： Rajesh Kumar Tamada , Junpyo Hong , Attila Márk Rádics , Hans Krupakar , Venkata Ratnam Saripalli , Dibyajyoti Pati , Guarav Kumar
Abstract: Techniques are provided for compressing deep neural networks using a structured filter pruning method that is extensible and effective. According to an embodiment, a computer-implemented method comprises determining, by a system operatively coupled to a processor, importance scores for filters of layers of a neural network model previously trained until convergence for an inferencing task on a training dataset. The method further comprises removing, by the system, a subset of the filters from one or more layers of the layers based on the importance scores associated with the subset failing to satisfy a threshold importance score value. The method further comprises converting, by the system, the neural network model into a compressed neural network model with the subset of the filters removed.
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公开(公告)号：US20250029370A1
公开(公告)日：2025-01-23
申请号：US18356461
申请日：2023-07-21
Applicant: GE Precision Healthcare LLC
Inventor： Xiaomeng Dong , Michael Potter , Hongxu Yang , Junpyo Hong , Ravi Soni , Gopal Biligeri Avinash
IPC: G06V10/774 , G06V10/776 , G06V10/82
Abstract: In various embodiments, a system can: access a failure image on which a first model has inaccurately performed an inferencing task; train, on a set of dummy images, a second model to learn a visual variety of the failure image, based on a loss function having a first term and a second term, the first term quantifying visual content dissimilarities between the set of dummy images and outputs predicted during training by the second model, and the second term quantifying, at a plurality of different image scales, visual variety dissimilarities between the failure image and the outputs predicted during training by the second model; and execute the second model on each of a set of training images on which the first model was trained, thereby yielding a set of first converted training images that exhibit the visual variety of the failure image.
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公开(公告)号：US20240108299A1
公开(公告)日：2024-04-04
申请号：US17937152
申请日：2022-09-30
Applicant: GE Precision Healthcare LLC
Inventor： Masaki Ikuta , Junpyo Hong , Rajesh Kumar Tamada , Ravi Soni
CPC classification number: A61B6/5258 , A61B6/5235 , G06T7/0012 , G06T2207/10081
Abstract: Computer processing techniques are described for augmenting computed tomography (CT) images with synthetic artifacts for artificial intelligence (AI) applications. According to an example, a computer-implemented method can include generating, by a system comprising a processor, synthetic artifact data corresponding to one or more CT image artifacts, wherein the synthetic artifact data comprises anatomy agnostic synthetic representations of the one or more CT image artifacts. The method further includes generating, by the system, augmented CT images comprising the one or more CT image artifacts using the synthetic artifact data. In one or more examples, the method can further include training, by the system, a medical image inferencing model to perform an inferencing task using the augmented CT images as training images.
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公开(公告)号：US20230169666A1
公开(公告)日：2023-06-01
申请号：US17457179
申请日：2021-12-01
Applicant: GE Precision Healthcare LLC
Inventor： Dibyajyoti Pati , Junpyo Hong , Venkata Ratnam Saripalli , German Guillermo Vera Gonzalez , Dejun Wang , Aizhen Zhou , Gopal B. Avinash , Ravi Soni , Tao Tan , Fuqiang Chen , Yaan Ge
CPC classification number: G06T7/30 , A61B6/5241 , G06N3/08 , G06T2207/20081 , G06T2207/20084 , G06T2207/10116 , G06T2207/20212
Abstract: Various methods and systems are provided for automatically registering and stitching images. In one example, a method includes entering a first image of a subject and a second image of the subject to a model trained to output a transformation matrix based on the first image and the second image, where the model is trained with a plurality of training data sets, each training data set including a pair of images, a mask indicating a region of interest (ROI), and associated ground truth, automatically stitching together the first image and the second image based on the transformation matrix to form a stitched image, and outputting the stitched image for display on a display device and/or storing the stitched image in memory.
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公开(公告)号：US20200327379A1
公开(公告)日：2020-10-15
申请号：US16699567
申请日：2019-11-30
Applicant: GE Precision Healthcare LLC
Inventor： Xiaomeng Dong , Aritra Chowdhury , Junpyo Hong , Hsi-Ming Chang , Gopal B. Avinash , Venkata Ratnam Saripalli , Karley Yoder , Michael Potter
Abstract: An artificial intelligence platform and associated methods of training and use are disclosed. An example apparatus includes a data pipeline to: preprocess data using one or more preprocessing operations applied to features associated with the data; and enable debugging to visualize the preprocessed data. The example apparatus includes a network to: instantiate one or more differentiable operations in a training configuration to train an artificial intelligence model; capture feedback including optimization and loss information to adjust the training configuration; and store one or more metrics to evaluate performance of the artificial intelligence model. The example apparatus includes an estimator to: store the training configuration for the artificial intelligence model; configure the pipeline and the network based on the training configuration; iteratively link the pipeline and the network based on the training configuration; and initiate training of the artificial intelligence model using the linked pipeline and network.
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