Patents by Inventor Gopal Avinash

Gopal Avinash has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11507822
    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.
    Type: Grant
    Filed: October 31, 2018
    Date of Patent: November 22, 2022
    Assignee: General Electric Company
    Inventors: Ravi Soni, Min Zhang, Gopal Avinash
  • Publication number: 20220284579
    Abstract: Apparatus, systems, and methods to improve imaging quality control, image processing, identification of findings, and generation of notification at or near a point of care are disclosed and described. An example imaging apparatus includes a processor to at least: process the first image data using a trained learning network to generate a first analysis of the first image data; identify a clinical finding in the first image data based on the first analysis; compare the first analysis to a second analysis, the second analysis generated from second image data obtained in a second image acquisition; and, when comparing identifies a change between the first analysis and the second analysis, generate a notification at the imaging apparatus regarding the clinical finding to trigger a responsive action.
    Type: Application
    Filed: May 23, 2022
    Publication date: September 8, 2022
    Inventors: Katelyn Rose Nye, Gireesha Rao, Gopal Avinash, Ravi Soni
  • Publication number: 20220284570
    Abstract: Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.
    Type: Application
    Filed: March 4, 2021
    Publication date: September 8, 2022
    Inventors: Tao Tan, Máté Fejes, Gopal Avinash, Ravi Soni, Bipul Das, Rakesh Mullick, Pál Tegzes, Lehel Ferenczi, Vikram Melapudi, Krishna Seetharam Shriram
  • Patent number: 11404145
    Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data processing are disclosed. An example time series event data processing apparatus includes memory storing instructions and one-dimensional time series healthcare-related data; and at least one processor. The example at least one processor is to: execute artificial intelligence model(s) trained on aggregated time series data to at least one of a) predict a future medical machine event, b) detect a medical machine event, or c) classify the medical machine event using the one-dimensional time series healthcare-related data; when the artificial intelligence model(s) are executed to predict the future medical machine event, output an alert related to the predicted future medical machine event to trigger a next action; and when the artificial intelligence model(s) are executed to detect and/or classify the medical machine event, label the medical machine event and output the labeled event to trigger the next action.
    Type: Grant
    Filed: November 27, 2019
    Date of Patent: August 2, 2022
    Assignee: GE Precision Healthcare LLC
    Inventors: Venkata Ratna Saripalli, Gopal Avinash, Min Zhang, Ravi Soni, Jiahui Guan, Dibyajyoti Pati, Zili Ma
  • Publication number: 20210232967
    Abstract: Methods and systems are provided for tuning a static model with multiple operating points to adjust model performance without retraining the model or triggering a new regulatory clearance. In one embodiment, a method comprises, responsive to a request to tune a model, obtaining a tuning dataset including a set of medical images, executing the model using the set of medical images as input to generate model tuning output, and determining, for each operating point of a set of operating points, a set of tuning metric values based on the tuning dataset and the model tuning output relative to each operating point. An operating point from the set of operating points may be selected based on each set of tuning metric values and, upon a request to analyze a subsequent medical image, a representation of a finding output from the static model executed at the selected operating point.
    Type: Application
    Filed: January 28, 2020
    Publication date: July 29, 2021
    Inventors: Katelyn Nye, Gopal Avinash, Pal Tegzes, Gireesha Rao
  • Patent number: 11074482
    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.
    Type: Grant
    Filed: July 14, 2020
    Date of Patent: July 27, 2021
    Assignee: General Electric Company
    Inventors: Qian Zhao, Min Zhang, Gopal Avinash
  • Patent number: 11003988
    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.
    Type: Grant
    Filed: November 23, 2016
    Date of Patent: May 11, 2021
    Assignee: General Electric Company
    Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey
  • Patent number: 10896352
    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.
    Type: Grant
    Filed: November 27, 2019
    Date of Patent: January 19, 2021
    Assignee: General Electric Company
    Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey, Xin Wang, Zhye Yin, Bruno De Man
  • Patent number: 10885400
    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 plurality of images is associated with a plurality of masks, a plurality of image level labels, and/or a bounding box. The system also generates a first loss function based on the plurality of masks, a second loss function based on the plurality of image level labels, and a third loss function based on the bounding box. Furthermore, the system generates a fourth loss function based on the first loss function, the second loss function and the third loss function, where the fourth 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.
    Type: Grant
    Filed: September 27, 2018
    Date of Patent: January 5, 2021
    Assignee: General Electric Company
    Inventors: Qian Zhao, Min Zhang, Gopal Avinash
  • Publication number: 20200402665
    Abstract: Techniques are described for predicting readmissions of patients to an inpatient healthcare facility. In an embodiment, a method comprises applying, by a system comprising a processor, applying, by a system operatively coupled to a processor, a readmission risk forecasting model to medical history data for a patient, wherein the readmission risk forecasting model comprises an attention-based graph neural network (A-GNN). The method further comprises, based on the applying, generating, by the system, a readmission risk score for the patient that reflects a probability of readmission of the patient following discharge from an inpatient healthcare facility. The method further comprises facilitating providing, by the system, the readmission risk score to at least one of the patient or a clinician involved in care of the patient.
    Type: Application
    Filed: June 19, 2020
    Publication date: December 24, 2020
    Inventors: Min Zhang, Gopal Avinash, Yrjö Häme, Ali Faisal, Kevin Leung, Jeff Hersh
  • Publication number: 20200349394
    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.
    Type: Application
    Filed: July 14, 2020
    Publication date: November 5, 2020
    Inventors: Qian Zhao, Min Zhang, Gopal Avinash
  • Publication number: 20200337648
    Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data processing are disclosed. An example time series event data processing apparatus includes memory storing instructions and one-dimensional time series healthcare-related data; and at least one processor. The example at least one processor is to: execute artificial intelligence model(s) trained on aggregated time series data to at least one of a) predict a future medical machine event, b) detect a medical machine event, or c) classify the medical machine event using the one-dimensional time series healthcare-related data; when the artificial intelligence model(s) are executed to predict the future medical machine event, output an alert related to the predicted future medical machine event to trigger a next action; and when the artificial intelligence model(s) are executed to detect and/or classify the medical machine event, label the medical machine event and output the labeled event to trigger the next action.
    Type: Application
    Filed: November 27, 2019
    Publication date: October 29, 2020
    Inventors: Venkata Ratna Saripalli, Gopal Avinash, Min Zhang, Ravi Soni, Jiahui Guan, Dibyajyoti Pati, Zili Ma
  • Patent number: 10811135
    Abstract: Apparatus, systems, and methods to improve automated identification, monitoring, processing, and control of a condition impacting a patient using image data and artificial intelligence classification are disclosed. An example image processing apparatus includes an artificial intelligence classifier to: process first image data for a patient from a first time to determine a first classification result indicating a first severity of a condition for the patient; and process second image data for the patient from a second time to determine a second classification result indicating a second severity of the condition for the patient. The example image processing apparatus includes a comparator to compare the first classification result and the second classification result to determine a change and a progression of the condition associated with the change. The example image processing apparatus includes an output generator to trigger an action when the progression corresponds to a worsening of the condition.
    Type: Grant
    Filed: December 27, 2018
    Date of Patent: October 20, 2020
    Assignee: General Electric Company
    Inventors: Katelyn Nye, Gireesha Rao, Gopal Avinash, Christopher Austin
  • Patent number: 10755147
    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.
    Type: Grant
    Filed: July 26, 2018
    Date of Patent: August 25, 2020
    Assignee: General Electric Company
    Inventors: Qian Zhao, Min Zhang, Gopal Avinash
  • Patent number: 10755140
    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.
    Type: Grant
    Filed: August 8, 2018
    Date of Patent: August 25, 2020
    Assignee: General Electric Company
    Inventors: Qian Zhao, Min Zhang, Gopal Avinash
  • Publication number: 20200211694
    Abstract: Apparatus, systems, and methods to improve automated identification, monitoring, processing, and control of a condition impacting a patient using image data and artificial intelligence classification are disclosed. An example image processing apparatus includes an artificial intelligence classifier to: process first image data for a patient from a first time to determine a first classification result indicating a first severity of a condition for the patient; and process second image data for the patient from a second time to determine a second classification result indicating a second severity of the condition for the patient. The example image processing apparatus includes a comparator to compare the first classification result and the second classification result to determine a change and a progression of the condition associated with the change. The example image processing apparatus includes an output generator to trigger an action when the progression corresponds to a worsening of the condition.
    Type: Application
    Filed: December 27, 2018
    Publication date: July 2, 2020
    Inventors: Katelyn Nye, Gireesha Rao, Gopal Avinash, Christopher Austin
  • Publication number: 20200134446
    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.
    Type: Application
    Filed: October 31, 2018
    Publication date: April 30, 2020
    Inventors: Ravi Soni, Min Zhang, Gopal Avinash
  • Patent number: 10628943
    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.
    Type: Grant
    Filed: October 9, 2018
    Date of Patent: April 21, 2020
    Assignee: General Electric Company
    Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey
  • Publication number: 20200097773
    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.
    Type: Application
    Filed: November 27, 2019
    Publication date: March 26, 2020
    Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey, Xin Wang, Zhye Yin, Bruno DeMan
  • Patent number: 10565477
    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.
    Type: Grant
    Filed: July 15, 2019
    Date of Patent: February 18, 2020
    Assignee: General Electric Company
    Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey, Xin Wang, Zhye Yin, Bruno De Man