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    SELF-SUPERVISED REPRESENTATION LEARNING PARADIGM FOR MEDICAL IMAGES
    23.
    发明申请
    SELF-SUPERVISED REPRESENTATION LEARNING PARADIGM FOR MEDICAL IMAGES 有权

    公开(公告)号：US20230111306A1

    公开(公告)日：2023-04-13

    申请号：US17500366

    申请日：2021-10-13

    Applicant: GE Precision Healthcare LLC

    Inventor： Deepa Anand ,  Annangi V. Pavan Kumar

    IPC: G06T7/00 ,  G06T7/11 ,  G06K9/32 ,  G06N3/08

    Abstract: Techniques are described for learning feature representations of medical images using a self-supervised learning paradigm and employing those feature representations for automating downstream tasks such as image retrieval, image classification and other medical image processing tasks. According to an embodiment, computer-implemented method comprises generating alternate view images for respective medical images included in set of training images using one or more image augmentation techniques or one or more image selection techniques tailored based on domain knowledge associated with the respective medical images. The method further comprises training a transformer network to learn reference feature representations for the respective medical images using their alternate view images and a self-supervised training process. The method further comprises storing the reference feature representations in an indexed data structure with information identifying the respective medical images that correspond to the reference feature representations.

    DATA DIVERSITY VISUALIZATION AND QUANTIFICATION FOR MACHINE LEARNING MODELS
    24.
    发明申请
    DATA DIVERSITY VISUALIZATION AND QUANTIFICATION FOR MACHINE LEARNING MODELS 有权

    公开(公告)号：US20220351055A1

    公开(公告)日：2022-11-03

    申请号：US17243046

    申请日：2021-04-28

    Applicant: GE Precision Healthcare LLC

    Inventor： Deepa Anand ,  Rakesh Mullick ,  Dattesh Dayanand Shanbhag ,  Marc T. Edgar

    IPC: G06N5/04 ,  G06N20/00

    Abstract: Systems and techniques that facilitate data diversity visualization and/or quantification for machine learning models are provided. In various embodiments, a processor can access a first dataset and a second dataset, where a machine learning (ML) model is trained on the first dataset. In various instances, the processor can obtain a first set of latent activations generated by the ML model based on the first dataset, and a second set of latent activations generated by the ML model based on the second dataset. In various aspects, the processor can generate a first set of compressed data points based on the first set of latent activations, and a second set of compressed data points based on the second set of latent activations, via dimensionality reduction. In various instances, a diversity component can compute a diversity score based on the first set of compressed data points and second set of compressed data points.

    AUTOMATION OF TRANSVAGINAL ULTRASOUND WORKFLOW
    25.
    发明申请
    AUTOMATION OF TRANSVAGINAL ULTRASOUND WORKFLOW 有权

    公开(公告)号：US20250152139A1

    公开(公告)日：2025-05-15

    申请号：US18505994

    申请日：2023-11-09

    Applicant: GE Precision Healthcare LLC

    Inventor： Pavan Annangi ,  Deepa Anand ,  Bhushan D. Patil ,  Stephan Anzengruber

    IPC: A61B8/08 ,  A61B8/00 ,  A61B8/12

    Abstract: The current disclosure provides systems and methods for improving a visualization of an image volume of a uterus and/or endometrium of a subject acquired using a transvaginal ultrasound system (TVUS). In one example, a method for the TVUS comprises extracting a medial axis of an endometrium of a received two-dimensional (2D) ultrasound image of a uterus of a subject; generating a uterine trace line based on the extracted medial axis; acquiring a three-dimensional (3D) image volume of the uterus based on the uterine trace line; and displaying the 3D image volume on a display device of the TVUS.

    Deep learning multi-planar reformatting of medical images
    26.
    发明授权
    Deep learning multi-planar reformatting of medical images 有权

    公开(公告)号：US12272023B2

    公开(公告)日：2025-04-08

    申请号：US17654864

    申请日：2022-03-15

    Applicant: GE Precision Healthcare LLC

    Inventor： Bipul Das ,  Rakesh Mullick ,  Deepa Anand ,  Sandeep Dutta ,  Uday Damodar Patil ,  Maud Bonnard

    IPC: G06T3/60 ,  G06T7/73 ,  G06V10/774 ,  G06V10/82 ,  G16H50/20

    Abstract: Systems/techniques that facilitate deep learning multi-planar reformatting of medical images are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can localize, via execution of a machine learning model, a set of landmarks depicted in the three-dimensional medical image, a set of principal anatomical planes depicted in the three-dimensional medical image, and a set of organs depicted in the three-dimensional medical image. In various instances, the system can determine an anatomical orientation exhibited by the three-dimensional medical image, based on the set of landmarks, the set of principal anatomical planes, or the set of organs. In various cases, the system can rotate the three-dimensional medical image, such that the anatomical orientation now matches a predetermined anatomical orientation.

    ENSEMBLED QUERYING OF EXAMPLE IMAGES VIA DEEP LEARNING EMBEDDINGS
    27.
    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.

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