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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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公开(公告)号：US10282836B2
公开(公告)日：2019-05-07
申请号：US15235523
申请日：2016-08-12
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Hariharan Ravishankar , Ravindra Mohan Manjeshwar , Floribertus PM Heukensfeldt Jansen , Michel Souheil Tohme
Abstract: According to one embodiment, a method of image analysis is provided. The method includes binning image data into a plurality of sinogram frames, identifying a plurality of initial stationary frames by applying a first analysis technique on the plurality of binned sinogram frames, extracting a plurality of first statistical parameters applying a second analysis technique on the plurality of binned sinogram frames, combining the plurality of first statistical parameters with boundaries of plurality of initial stationary frames to generate a presentation of a joint analysis combining at least some of the plurality of the first statistical parameters and at least some of the plurality of the second statistical parameter, identifying a plurality of final stationary frames from the presentation of the joint analysis, independently reconstructing each of the plurality of final stationary frames, and registering each of the plurality of final stationary frames to a first state.
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公开(公告)号：US20220366321A1
公开(公告)日：2022-11-17
申请号：US17864694
申请日：2022-07-14
Applicant: General Electric Company
Inventor： Arathi Sreekumari , Radhika Madhavan , Suresh Emmanuel Joel , Hariharan Ravishankar
Abstract: A method for controlling a physical process includes receiving an input dataset corresponding to the physical process. The method further includes determining a data model based on the input dataset. The data model includes a plurality of latent space variables of a machine learning model. The method also includes receiving a plurality of reference models corresponding to a plurality of classes. Each of the plurality of reference models includes a corresponding plurality of latent space variables. The method includes comparing the data model with each of the plurality of reference models to generate a plurality of distance metric values. The method further includes selecting a reference model among the plurality of reference models based on the plurality of distance metric values. The method also includes controlling the physical process based on the selected reference model.
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公开(公告)号：US11410086B2
公开(公告)日：2022-08-09
申请号：US16282592
申请日：2019-02-22
Applicant: General Electric Company
Inventor： Arathi Sreekumari , Radhika Madhavan , Suresh Emmanuel Joel , Hariharan Ravishankar
Abstract: A method for controlling a physical process includes receiving an input dataset corresponding to the physical process. The method further includes determining a data model based on the input dataset. The data model includes a plurality of latent space variables of a machine learning model. The method also includes receiving a plurality of reference models corresponding to a plurality of classes. Each of the plurality of reference models includes a corresponding plurality of latent space variables. The method includes comparing the data model with each of the plurality of reference models to generate a plurality of distance metric values. The method further includes selecting a reference model among the plurality of reference models based on the plurality of distance metric values. The method also includes controlling the physical process based on the selected reference model.
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公开(公告)号：US20190005384A1
公开(公告)日：2019-01-03
申请号：US15637837
申请日：2017-06-29
Applicant: General Electric Company
Inventor： Bharath Ram Sundar , Radhika Madhavan , Hariharan Ravishankar
IPC: G06N3/08
Abstract: The present approach relates to the processing of edge information related to graph topology using a neural network. In one aspect, graph topology information along with edge weights are added as a first hidden layer of a neural network. In this manner, better spatial information is transferred to the neural network.
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Patent Agency Ranking
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：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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公开(公告)号：US20190258962A1
公开(公告)日：2019-08-22
申请号：US16282592
申请日：2019-02-22
Applicant: General Electric Company
Inventor： Arathi Sreekumari , Radhika Madhavan , Suresh Emmanuel Joel , Hariharan Ravishankar
Abstract: A method for controlling a physical process includes receiving an input dataset corresponding to the physical process. The method further includes determining a data model based on the input dataset. The data model includes a plurality of latent space variables of a machine learning model. The method also includes receiving a plurality of reference models corresponding to a plurality of classes. Each of the plurality of reference models includes a corresponding plurality of latent space variables. The method includes comparing the data model with each of the plurality of reference models to generate a plurality of distance metric values. The method further includes selecting a reference model among the plurality of reference models based on the plurality of distance metric values. The method also includes controlling the physical process based on the selected reference model.
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公开(公告)号：US20150157275A1
公开(公告)日：2015-06-11
申请号：US14101663
申请日：2013-12-10
Applicant: General Electric Company
Inventor： Gokul Swamy , Sahika Genc , Hariharan Ravishankar , Aditya Saha
IPC: A61B5/00 , A61B5/20 , A61B5/145 , A61B5/0205 , A61B5/1455
CPC classification number: A61B5/7275 , A61B5/002 , A61B5/0022 , A61B5/01 , A61B5/02055 , A61B5/021 , A61B5/024 , A61B5/0816 , A61B5/0836 , A61B5/14532 , A61B5/14542 , A61B5/201 , A61B5/202 , A61B5/7203 , A61B5/7246 , A61B5/7264 , A61B5/746 , G06F19/00
Abstract: Embodiments of the disclosure are directed to a system for analysis of respiratory distress in hospitalized patients. The system performs multi-parametric simultaneous analysis of respiration rate (RR) and pulse oximetry (SpO2) data trends in order to gauge patterns of patient instability pertaining to respiratory distress. Three patterns in SpO2 and RR are used along with LOWESS algorithm and Chauvenets criteria for outlier rejection to obtain robust short term and long term trends in RR and SpO2. Pattern analysis detects the presence of any one of three pattern types proposed. Further, a learning paradigm is introduced to find unknown instances of respiratory distress. This algorithm in conjunction with the learning model allows early detection of respiratory distress in hospital ward and ICU patients.
Abstract translation: 本公开的实施例涉及用于分析住院患者呼吸窘迫的系统。 系统进行呼吸速率（RR）和脉搏血氧饱和度（SpO2）数据趋势的多参数同时分析，以衡量与呼吸窘迫有关的患者不稳定状态。 使用SpO2和RR中的三种模式以及LOWESS算法和Chauvenets等离子体排斥标准，以获得RR和SpO2的强大的短期和长期趋势。 模式分析检测提出的三种模式类型中的任一种的存在。 此外，引入了一种学习范例来发现呼吸窘迫的未知事例。 该算法结合学习模型，可以及早发现医院病房和ICU患者的呼吸窘迫。
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