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公开(公告)号：US11810301B2
公开(公告)日：2023-11-07
申请号：US17227093
申请日：2021-04-09
Applicant: General Electric Company
Inventor： Harihan Ravishankar , Vivek Vaidya , Sheshadri Thiruvenkadam , Rahul Venkataramani , Prasad Sudhakar
CPC classification number: G06T7/10 , G06F17/15 , G06N3/045 , G06T2207/20084
Abstract: A method for image segmentation includes receiving an input image. The method further includes obtaining a deep learning model having a triad of predictors. Furthermore, the method includes processing the input image by a shape model in the triad of predictors to generate a segmented shape image. Moreover, the method includes presenting the segmented shape image via a display unit.
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公开(公告)号：US11232344B2
公开(公告)日：2022-01-25
申请号：US15799698
申请日：2017-10-31
Applicant: General Electric Company
Inventor： Hariharan Ravishankar , Bharath Ram Sundar , Prasad Sudhakar , Rahul Venkataramani , Vivek Vaidya
Abstract: The present approach relates to feature ranking within deep neural networks in a multi-task and/or multi-label setting. Approaches are described to identify features that are task-specific as well as features that are shared across multiple tasks. In addition to facilitating interpretability, the selected subset of features can be used to make efficient models leading to better stability & regularization along with reduced compute and memory.
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公开(公告)号：US20190130247A1
公开(公告)日：2019-05-02
申请号：US15799698
申请日：2017-10-31
Applicant: General Electric Company
Inventor： Hariharan Ravishankar , Bharath Ram Sundar , Prasad Sudhakar , Rahul Venkataramani , Vivek Vaidya
Abstract: The present approach relates to feature ranking within deep neural networks in a multi-task and/or multi-label setting. Approaches are described to identify features that are task-specific as well as features that are shared across multiple tasks. In addition to facilitating interpretability, the selected subset of features can be used to make efficient models leading to better stability & regularization along with reduced compute and memory.
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公开(公告)号：US11017269B2
公开(公告)日：2021-05-25
申请号：US16334091
申请日：2017-06-21
Applicant: General Electric Company
Inventor： Sheshadri Thiruvenkadam , Sohan Rashmi Ranjan , Vivek Prabhakar Vaidya , Hariharan Ravishankar , Rahul Venkataramani , Prasad Sudhakar
Abstract: A method for determining optimized deep learning architecture includes receiving a plurality of training images and a plurality of real time images corresponding to a subject. The method further includes receiving, by a medical practitioner, a plurality of learning parameters comprising a plurality of filter classes and a plurality of architecture parameters. The method also includes determining a deep learning model based on the plurality of learning parameters and the plurality of training images, wherein the deep learning model comprises a plurality of reusable filters. The method further includes determining a health condition of the subject based on the plurality of real time images and the deep learning model. The method also includes providing the health condition of the subject to the medical practitioner.
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公开(公告)号：US11651584B2
公开(公告)日：2023-05-16
申请号：US16161061
申请日：2018-10-16
Applicant: General Electric Company
Inventor： Rahul Venkataramani , Rakesh Mullick , Sandeep Kaushik , Hariharan Ravishankar , Sai Hareesh Anamandra
IPC: G06V10/764 , G06N20/00 , G06F16/583 , G06F16/51 , G06V10/776 , G06V10/82 , G06V10/94
CPC classification number: G06V10/764 , G06F16/51 , G06F16/5838 , G06N20/00 , G06V10/776 , G06V10/82 , G06V10/94
Abstract: A system is presented. The system includes an acquisition subsystem configured to obtain images corresponding to a target domain. Moreover, the system includes a processing subsystem in operative association with the acquisition subsystem and including a memory augmented domain adaptation platform configured to compute one or more features of an input image corresponding to a target domain, identify a set of support images based on the features of the input image, where the set of support images corresponds to the target domain, augment an input to a machine-learnt model with a set of features, a set of masks, or both corresponding to the set of support images to adapt the machine-learnt model to the target domain, and generate an output based at least on the set of features, the set of masks, or both. Additionally, the system includes an interface unit configured to present the output for analysis.
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Patent Agency Ranking
公开(公告)号：US20200315569A1
公开(公告)日：2020-10-08
申请号：US16372446
申请日：2019-04-02
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Suvadip Mukherjee , Rahul Venkataramani , Anuprriya Gogna , Stephan Anzengruber
Abstract: A method for determining a nervous system condition includes obtaining an estimate of a first scan plane among a plurality of planes of a maternal subject using a first deep learning network during a guided scanning procedure. The method further includes receiving a three-dimensional (3D) ultrasound volume corresponding to the initial estimate and determining an optimal first scan plane from the first deep learning network. The method further includes determining at least one of a second scan plane, a third scan plane and a fourth scan plane among the plurality of planes, based on the optimal first scan plane and at least one of a clinical constraint corresponding to the plurality of planes using a second deep learning network. The method includes determining a biometric parameter corresponding to nervous system based on at least one of the plurality of planes using a third deep learning network.
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公开(公告)号：US11605455B2
公开(公告)日：2023-03-14
申请号：US16722409
申请日：2019-12-20
Applicant: General Electric Company
Abstract: The subject matter discussed herein relates to systems and methods for generating a clinical outcome based on creating a task-specific model associated with processing raw image(s). In one such example, input raw data is acquired using an imaging system, a selection input corresponding to a clinical task is received, and a task-specific model corresponding to the clinical task is retrieved. Using the task-specific model, the raw data is mapped onto an application specific manifold. Based on the mapping of the raw data onto the application specific manifold the clinical outcome is generated, and subsequently providing the clinical outcome for review.
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公开(公告)号：US20210233244A1
公开(公告)日：2021-07-29
申请号：US17227093
申请日：2021-04-09
Applicant: General Electric Company
Inventor： Harihan Ravishankar , Vivek Vaidya , Sheshadri Thiruvenkadam , Rahul Venkataramani , Prasad Sudhakar
Abstract: A method for image segmentation includes receiving an input image. The method further includes obtaining a deep learning model having a triad of predictors. Furthermore, the method includes processing the input image by a shape model in the triad of predictors to generate a segmented shape image. Moreover, the method includes presenting the segmented shape image via a display unit.
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公开(公告)号：US10997724B2
公开(公告)日：2021-05-04
申请号：US16469373
申请日：2017-12-14
Applicant: General Electric Company
Inventor： Hariharan Ravishankar , Vivek Prabhakar Vaidya , Sheshadri Thiruvenkadam , Rahul Venkataramani , Prasad Sudhakar
Abstract: A method for image segmentation includes receiving an input image (102). The method further includes obtaining a deep learning model (104) having a triad of predictors (116, 118, 120). Furthermore, the method includes processing the input image by a shape model in the triad of predictors (116, 118, 120) to generate a segmented shape image (110). Moreover, the method includes presenting the segmented shape image via a display unit (128).
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公开(公告)号：US20190266448A1
公开(公告)日：2019-08-29
申请号：US16334091
申请日：2017-06-21
Applicant: General Electric Company
Inventor： Sheshadri Thiruvenkadam , Sohan Rashmi Ranjan , Vivek Prabhakar Vaidya , Hariharan Ravishankar , Rahul Venkataramani , Prasad Sudhakar
Abstract: A method for determining optimized deep learning architecture includes receiving a plurality of training images and a plurality of real time images corresponding to a subject. The method further includes receiving, by a medical practitioner, a plurality of learning parameters comprising a plurality of filter classes and a plurality of architecture parameters. The method also includes determining a deep learning model based on the plurality of learning parameters and the plurality of training images, wherein the deep learning model comprises a plurality of reusable filters. The method further includes determining a health condition of the subject based on the plurality of real time images and the deep learning model. The method also includes providing the health condition of the subject to the medical practitioner.
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