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11.发明申请公开(公告)号：US20190340470A1
公开(公告)日：2019-11-07
申请号：US16511972
申请日：2019-07-15
Applicant: General Electric Company
Inventor： Jiang Hsieh , Gopal Avinash , Saad Sirohey , Xin Wang , Zhye Yin , Bruno De Man
Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.
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12.发明授权公开(公告)号：US10354171B2
公开(公告)日：2019-07-16
申请号：US16126762
申请日：2018-09-10
Applicant: General Electric Company
Inventor： Jiang Hsieh , Gopal Avinash , Saad Sirohey , Xin Wang , Zhye Yin , Bruno De Man
Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.
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公开(公告)号：US20180144243A1
公开(公告)日：2018-05-24
申请号：US15360042
申请日：2016-11-23
Applicant: General Electric Company
Inventor： Jiang Hsieh , Gopal Avinash , Saad Sirohey
CPC classification number: G06N3/08 , G06F11/30 , G06F17/5009 , G06N3/0454
Abstract: Methods and apparatus for deep learning-based system design improvement are provided. An example system design engine apparatus includes a deep learning network (DLN) model associated with each component of a target system to be emulated, each DLN model to be trained using known input and known output, wherein the known input and known output simulate input and output of the associated component of the target system, and wherein each DLN model is connected as each associated component to be emulated is connected in the target system to form a digital model of the target system. The example apparatus also includes a model processor to simulate behavior of the target system and/or each component of the target system to be emulated using the digital model to generate a recommendation regarding a configuration of a component of the target system and/or a structure of the component of the target system.
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14.发明申请APPARATUS, SYSTEM AND METHODS FOR ASSESSING DRUG EFFICACY USING HOLISTIC ANALYSIS AND VISUALIZATION OF PHARMACOLOGICAL DATA 审中-公开用于评估药物分析和药物数据可视化的药物效果的装置，系统和方法公开(公告)号：US20140195170A1
公开(公告)日：2014-07-10
申请号：US14151383
申请日：2014-01-09
Applicant: General Electric Company
Inventor： Gopal Avinash , Ananth Mohan , Zhongmin Lin , Rick Wascher
IPC: G01N33/15
Abstract: Certain examples provide systems and methods for holistic viewing to provide comparative analysis and decision support in a drug development process. An example method includes a computer-implemented method for assessing drug efficacy, comprising: accessing a first data set related to the performance of a target drug for a given indication; accessing a second data set related to a control for the indication; comparing the data for the target drug and the data for the control on at least one of a plurality of different metrics using a holistic analysis, wherein the at least one metric corresponds to an outcome associated with the indication and generating a corresponding report. An example apparatus/system includes a holistic analysis and viewing system to support the assessment of drug efficacy, said system comprising: a standardizer to at least one of standardize and normalize data related to drug development; a deviation analyzer to analyze said data based on at least one of a plurality of different efficacy metrics, wherein a quantified variation between a first data set of results corresponding to an identified target drug and a second data set of results corresponding to a control, wherein said first data set of results is provided for comparison with the second data set of results and the deviation therebetween is compared to the at least one efficacy metric.
Abstract translation: 某些例子提供了整体观察的系统和方法，以在药物开发过程中提供比较分析和决策支持。 示例性方法包括用于评估药物功效的计算机实现的方法，包括：访问与给定指示的目标药物的性能相关的第一数据集; 访问与所述指示的控制相关的第二数据集; 使用整体分析来比较目标药物的数据和用于多个不同度量中的至少一个的用于控制的数据，其中所述至少一个度量对应于与所述指示相关联的结果并生成相应的报告。 示例性装置/系统包括支持药物功效评估的整体分析和观察系统，所述系统包括：标准化器，其至少一个标准化和归一化与药物开发有关的数据; 偏差分析器，用于基于多个不同功效度量中的至少一个来分析所述数据，其中对应于所识别的目标药物的第一结果数据集与对照于对照的结果的第二数据集之间的量化变化，其中 提供所述第一数据结果数据集用于与第二数据结果集进行比较，并且将它们之间的偏差与至少一个功效度量进行比较。
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公开(公告)号：US11507822B2
公开(公告)日：2022-11-22
申请号：US16176980
申请日：2018-10-31
Applicant: General Electric Company
Inventor： Ravi Soni , Min Zhang , Gopal Avinash
Abstract: Systems and methods to generate artificial intelligence models with synthetic data are disclosed. An example system includes a deep neural network (DNN) generator to generate a first DNN model using first real data. The example system includes a synthetic data generator to generate first synthetic data from the first real data, the first synthetic data to be used by the DNN generator to generate a second DNN model. The example system includes an evaluator to evaluate performance of the first and second DNN models to determine whether to generate second synthetic data. The example system includes a synthetic data aggregator to aggregate third synthetic data and fourth synthetic data from a plurality of sites to form a synthetic data set. The example system includes an artificial intelligence model deployment processor to deploy an artificial intelligence model trained and tested using the synthetic data set.
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Patent Agency Ranking
公开(公告)号：US11074482B2
公开(公告)日：2021-07-27
申请号：US16928462
申请日：2020-07-14
Applicant: General Electric Company
Inventor： Qian Zhao , Min Zhang , Gopal Avinash
IPC: G06K9/62
Abstract: Systems and techniques for classification and localization based on annotation information are presented. In one example, a system trains a convolutional neural network based on training data and a plurality of images. The training data is associated with a plurality of patients from at least one imaging device. The plurality of images is associated with a plurality of masks from a plurality of objects. The convolutional neural network comprises a decoder consisting of at least one up-sampling layer and at least one convolutional layer. The system also generates a loss function based on the plurality of masks, where the loss function is iteratively back propagated to tune parameters of the convolutional neural network. The system also predicts a classification label for an input image based on the convolutional neural network.
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17.发明授权公开(公告)号：US10896352B2
公开(公告)日：2021-01-19
申请号：US16697904
申请日：2019-11-27
Applicant: General Electric Company
Inventor： Jiang Hsieh , Gopal Avinash , Saad Sirohey , Xin Wang , Zhye Yin , Bruno De Man
Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.
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公开(公告)号：US10755140B2
公开(公告)日：2020-08-25
申请号：US16058984
申请日：2018-08-08
Applicant: General Electric Company
Inventor： Qian Zhao , Min Zhang , Gopal Avinash
Abstract: Systems and techniques for classification based on annotation information are presented. In one example, a system trains a convolutional neural network based on training data and a plurality of images. The training data is associated with a plurality of patients from at least one imaging device. The plurality of images is associated with a plurality of masks from a plurality of objects. The system also generates a loss function based on the plurality of masks, where the loss function is iteratively back propagated to tune parameters of the convolutional neural network. The system also predicts a classification label for an input image based on the convolutional neural network.
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19.发明授权公开(公告)号：US10565477B2
公开(公告)日：2020-02-18
申请号：US16511972
申请日：2019-07-15
Applicant: General Electric Company
Inventor： Jiang Hsieh , Gopal Avinash , Saad Sirohey , Xin Wang , Zhye Yin , Bruno De Man
Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.
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公开(公告)号：US20190050987A1
公开(公告)日：2019-02-14
申请号：US16154870
申请日：2018-10-09
Applicant: General Electric Company
Inventor： Jiang Hsieh , Gopal Avinash , Saad Sirohey
Abstract: Methods and apparatus for improved deep learning for image acquisition are provided. An imaging system configuration apparatus includes a training learning device including a first processor to implement a first deep learning network (DLN) to learn a first set of imaging system configuration parameters based on a first set of inputs from a plurality of prior image acquisitions to configure at least one imaging system for image acquisition, the training learning device to receive and process feedback including operational data from the plurality of image acquisitions by the at least one imaging system. The example apparatus includes a deployed learning device including a second processor to implement a second DLN, the second DLN generated from the first DLN of the training learning device, the deployed learning device configured to provide a second imaging system configuration parameter to the imaging system in response to receiving a second input for image acquisition.
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