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    DOMAIN ADAPTATION USING PSEUDO-LABELLING AND MODEL CERTAINTY QUANTIFICATION FOR VIDEO DATA
    21.
    发明申请
    DOMAIN ADAPTATION USING PSEUDO-LABELLING AND MODEL CERTAINTY QUANTIFICATION FOR VIDEO DATA 有权

    公开(公告)号：US20220301287A1

    公开(公告)日：2022-09-22

    申请号：US17654019

    申请日：2022-03-08

    Applicant: GE Precision Healthcare LLC

    Inventor： Pavan Annangi ,  Deepa Anand

    IPC: G06V10/764 ,  G06V10/774 ,  G06T7/00

    Abstract: Systems and method for domain adaptation using pseudo-labelling and model certainty quantification for video data are provided. The method includes obtaining a source data and a target data each comprising a plurality of frames for processing by a machine learning module. The method comprises testing the target data to identify if a minimum number of frames exhibit a frame confidence score based on the source data and identifying salient region within the target data and measuring a degree of spatial consistency of the salient region over time. The method comprises identifying class specific attention region within the target data and measuring a confidence score of class specific attention region within the target data and carrying out pseudo-labeling of the target data based on the source data and calculating a certainty metrics value based on the frame confidence score, the degree of spatial consistency of the salient region over time, the confidence score of class specific attention region within the frames of the target data and confidence score of the pseudo-labeling on the target data. The machine learning module is retrained till the certainty metrics value reaches peak and further retraining the machine learning module does not increase the certainty metrics value.

    Self-supervised representation learning paradigm for medical images
    25.
    发明授权
    Self-supervised representation learning paradigm for medical images 有权

    公开(公告)号：US12211202B2

    公开(公告)日：2025-01-28

    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 ,  G06N3/047 ,  G06N3/0475 ,  G06N3/088 ,  G06N3/0895 ,  G06N3/096 ,  G06T7/11 ,  G06V10/25 ,  G06V10/74 ,  G06V10/762 ,  G06V10/774 ,  G06V10/82

    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.

    ULTRASOUND IMAGING SYSTEM AND METHOD FOR SEGMENTING AN OBJECT FROM A VOLUMETRIC ULTRASOUND DATASET
    26.
    发明公开
    ULTRASOUND IMAGING SYSTEM AND METHOD FOR SEGMENTING AN OBJECT FROM A VOLUMETRIC ULTRASOUND DATASET 审中-公开

    公开(公告)号：US20240285256A1

    公开(公告)日：2024-08-29

    申请号：US18175307

    申请日：2023-02-27

    Applicant: GE Precision Healthcare LLC

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

    IPC: A61B8/08 ,  A61B8/00

    CPC classification number: A61B8/483 ,  A61B8/466 ,  G06T2207/20084

    Abstract: Various methods and ultrasound imaging systems are provided for segmenting an object. In one example, a method includes accessing a volumetric ultrasound dataset, receiving an identification of a seed point for an object in an image generated based on the volumetric ultrasound dataset, and implementing a two-dimensional segmentation model on a first plurality of parallel slices based on the seed point to generate a first plurality of segmented regions. The method includes implementing the two-dimensional segmentation model on a second plurality of parallel slices based on the seed point to generate a second plurality of segmented regions. The method includes generating a detected region by accumulating the first plurality of segmented regions and the second plurality of segmented regions. The method includes implementing a shape completion model to generate a three-dimensional shape model for the object, and displaying rendering of the object based on the three-dimensional shape model.

    INTERPRETABLE TASK-SPECIFIC DIMENSIONALITY REDUCTION
    27.
    发明公开
    INTERPRETABLE TASK-SPECIFIC DIMENSIONALITY REDUCTION 审中-公开

    公开(公告)号：US20240203039A1

    公开(公告)日：2024-06-20

    申请号：US18065964

    申请日：2022-12-14

    Applicant: GE Precision Healthcare LLC

    Inventor： Deepa Anand ,  Bipul Das ,  Vanika Singhal ,  Rakesh Mullick ,  Sanjay Kumar NT

    IPC: G06T15/20 ,  G06T15/08 ,  G06V10/82

    CPC classification number: G06T15/20 ,  G06T15/08 ,  G06V10/82

    Abstract: Systems/techniques that facilitate interpretable task-specific dimensionality-reduction are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can generate, via execution of a first deep learning neural network, a voxel-wise weight map corresponding to the three-dimensional medical image and a set of projection vectors corresponding to the three-dimensional medical image. In various instances, the system can generate a set of two-dimensional projection images of the three-dimensional medical image, based on the voxel-wise weight map and the set of projection vectors. In various cases, the first deep learning neural network can be trained in a serial pipeline with a second deep learning neural network that is configured to perform an inferencing task on two-dimensional inputs. This can cause the set of two-dimensional projection images to be tailored to the inferencing task.

    SELF-SUPERVISED REPRESENTATION LEARNING PARADIGM FOR MEDICAL IMAGES
    30.
    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.

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