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    ENSEMBLED QUERYING OF EXAMPLE IMAGES VIA DEEP LEARNING EMBEDDINGS
    1.
    发明申请
    ENSEMBLED QUERYING OF EXAMPLE IMAGES VIA DEEP LEARNING EMBEDDINGS 有权

    公开(公告)号：US20250095826A1

    公开(公告)日：2025-03-20

    申请号：US18470734

    申请日：2023-09-20

    Applicant: GE Precision Healthcare LLC

    Inventor： Vikram Reddy Melapudi ,  Hariharan Ravishankar ,  Deepa Anand

    IPC: G16H30/40 ,  G06F3/14 ,  G06T7/11 ,  G06T7/70 ,  G06V10/82

    Abstract: Systems or techniques that facilitate ensembled querying of example images via deep learning embeddings are provided. In various embodiments, a system can access a medical image associated with a medical patient. In various aspects, the system can generate an ensembled heat map indicating where in the medical image an anatomical structure is likely to be located, by executing an embedder neural network on the medical image and on a plurality of example medical images associated with other medical patients. In various instances, respective instantiations of the anatomical structure can be flagged in the plurality of example medical images by user-provided clicks.

    DISTILLATION OF DEEP ENSEMBLES
    2.
    发明公开
    DISTILLATION OF DEEP ENSEMBLES 审中-公开

    公开(公告)号：US20240281649A1

    公开(公告)日：2024-08-22

    申请号：US18170888

    申请日：2023-02-17

    Applicant: GE Precision Healthcare LLC

    Inventor： Hariharan Ravishankar ,  Prasad Sudhakara Murthy ,  Rohan Patil

    IPC: G06N3/08 ,  G06N5/04

    CPC classification number: G06N3/08 ,  G06N5/04

    Abstract: Systems/techniques that facilitate improved distillation of deep ensembles are provided. In various embodiments, a system can access a deep learning ensemble configured to perform an inferencing task. In various aspects, the system can iteratively distill the deep learning ensemble into a smaller deep learning ensemble configured to perform the inferencing task, wherein a current distillation iteration can involve training a new neural network of the smaller deep learning ensemble via a loss function that is based on one or more neural networks of the smaller deep learning ensemble which were trained during one or more previous distillation iterations.

    System and method for using a deep learning network over time
    3.
    发明授权
    System and method for using a deep learning network over time 有权

    公开(公告)号：US11580384B2

    公开(公告)日：2023-02-14

    申请号：US16522367

    申请日：2019-07-25

    Applicant: GE Precision Healthcare LLC

    Inventor： Rahul Venkataramani ,  Sai Hareesh Anamandra ,  Hariharan Ravishankar ,  Prasad Sudhakar

    IPC: G06N3/08 ,  G06T7/00 ,  G06N3/04

    Abstract: The present approach relates to a system capable of life-long learning in a deep learning context. The system includes a deep learning network configured to process an input dataset and perform one or more tasks from among a first set of tasks. As an example, the deep learning network may be part of an imaging system, such as a medical imaging system, or may be used in industrial applications. The system further includes a learning unit communicatively coupled to the deep learning network 102 and configured to modify the deep learning network so as to enable it to perform one or more tasks in a second task list without losing the ability to perform the tasks from the first list.

    SYSTEM AND METHODS FOR ELECTROCARDIOGRAM BEAT SIMILARITY ANALYSIS
    7.
    发明公开
    SYSTEM AND METHODS FOR ELECTROCARDIOGRAM BEAT SIMILARITY ANALYSIS 审中-公开

    公开(公告)号：US20230181082A1

    公开(公告)日：2023-06-15

    申请号：US18167693

    申请日：2023-02-10

    Applicant: GE Precision Healthcare LLC

    Inventor： Hariharan Ravishankar ,  Rahul Venkataramani

    IPC: A61B5/335 ,  A61B5/00

    CPC classification number: A61B5/335 ,  A61B5/7246 ,  A61B5/7267

    Abstract: Methods and systems are provided for determining a phase shift and noise insensitive similarity metric for electrocardiogram (ECG) beats in a Holter monitor recording. In one embodiment, a method includes selecting a first beat and a second beat recorded via one or more Holter monitors, determining a dynamic time warping (DTW) distance between the first beat and the second beat, setting a similarity label for the first beat and the second beat based on the DTW distance, and storing the first beat, the second beat, and the similarity label, in a location of non-transitory memory as an ECG training data triad, and training a machine learning model with the ECG training data triad.

    System and methods for electrocardiogram beat similarity analysis using deep neural networks
    8.
    发明授权
    System and methods for electrocardiogram beat similarity analysis using deep neural networks 有权

    公开(公告)号：US11589828B2

    公开(公告)日：2023-02-28

    申请号：US16733797

    申请日：2020-01-03

    Applicant: GE Precision Healthcare LLC

    Inventor： Hariharan Ravishankar ,  Rahul Venkataramani

    IPC: G06N3/084 ,  G16H50/20 ,  G16H40/63 ,  G16H10/60 ,  A61B5/316 ,  A61B5/00 ,  G06N3/04

    Abstract: Methods and systems are provided for automatically determining a phase shift and noise insensitive similarity metric for electrocardiogram (ECG) beats in a Holter monitor recording. In one embodiment, a deep neural network may be trained to map an ECG beat to a phase shift insensitive and noise insensitive feature space embedding using a training data triad, wherein the training data triad may be produced by a method comprising: selecting a first beat and a second beat recorded via one or more Holter monitors, determining a dynamic time warping (DTW) distance between the first beat and the second beat, setting a similarity label for the first beat and the second beat based on the DTW distance, and storing the first beat, the second beat, and the similarity label, in a location of non-transitory memory as an ECG training data triad.

    Model suitability coefficients based on generative adversarial networks and activation maps
    10.
    发明授权
    Model suitability coefficients based on generative adversarial networks and activation maps 有权

    公开(公告)号：US12229685B2

    公开(公告)日：2025-02-18

    申请号：US17155997

    申请日：2021-01-22

    Applicant: GE Precision Healthcare LLC

    Inventor： Hariharan Ravishankar ,  Rahul Venkataramani ,  Prasad Sudhakara Murthy ,  Annangi P. Pavan Kumar

    IPC: G06N3/088 ,  G06N3/045

    Abstract: Systems/techniques that facilitate generation of model suitability coefficients are provided. In various embodiments, a system can access a model trained on a training dataset, and the system can compute a coefficient indicating whether the model is suitable for deployment on a target dataset, based on analyzing activation maps associated with the model. In some cases, the system can: train a generative adversarial network (GAN) to learn a distribution of training activation maps produced by the model; generate a set of target activation maps of the model, by feeding samples from the target dataset to the model; cause a generator of the GAN to generate synthetic training activation maps from the learned distribution of training activation maps; iteratively perturb inputs of the generator until distances between the synthetic training activation maps and the target activation maps are minimized; and aggregate the minimized distances to form the coefficient.

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