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    DATA DIVERSITY VISUALIZATION AND QUANTIFICATION FOR MACHINE LEARNING MODELS
    22.
    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.

    SYSTEM AND METHOD FOR IDENTIFYING A TUMOR OR LESION IN A PROBABILTY MAP
    24.
    发明申请
    SYSTEM AND METHOD FOR IDENTIFYING A TUMOR OR LESION IN A PROBABILTY MAP 有权

    公开(公告)号：US20220067919A1

    公开(公告)日：2022-03-03

    申请号：US17003467

    申请日：2020-08-26

    Applicant: GE Precision Healthcare LLC

    Inventor： Krishna Seetharam Shriram ,  Arathi Sreekumari ,  Rakesh Mullick

    IPC: G06T7/00 ,  G06K9/32 ,  G06T11/00

    Abstract: The present disclosure relates to a system and method for identifying a tumor or lesion in a probability map. In accordance with certain embodiments, a method includes identifying, with a processor, a first region of interest in a first projection image, generating, with the processor, a first probability map from the first projection image and a second probability map from a second projection image, wherein the first probability map includes a second region of interest that has location that corresponds to a location of the first region of interest, interpolating the first probability map and the second probability map, thereby generating a probability volume, wherein the probability volume includes the second region of interest, and outputting, with the processor, a representation of the probability volume to a display.

    METHODS AND SYSTEMS FOR COMPUTER-AIDED DIAGNOSIS WITH DEEP LEARNING MODELS
    25.
    发明申请
    METHODS AND SYSTEMS FOR COMPUTER-AIDED DIAGNOSIS WITH DEEP LEARNING MODELS 有权

    公开(公告)号：US20210065899A1

    公开(公告)日：2021-03-04

    申请号：US16557797

    申请日：2019-08-30

    Applicant: GE Precision Healthcare LLC

    Inventor： Chandan Kumar Mallappa Aladahalli ,  Rakesh Mullick ,  James William Gualtieri

    IPC: G16H50/20 ,  G06N3/08

    Abstract: Various methods and systems are provided for computer-aided diagnosis. In one embodiment, a method comprises acquiring, with an imaging system, a medical image of a subject, generating, with a radiologist model associated with a radiologist of an institution, a computer-aided diagnosis for the medical image, the radiologist model comprising a deep neural network trained on a plurality of diagnoses provided by the radiologist, displaying, to the radiologist via a display device, the medical image and the computer-aided diagnosis, and selectively updating, based on the medical image, one or more of the radiologist model, an institution model associated with the institution, and a geographic model associated with a geographic area containing the institution. In this way, a radiologist may be assisted by a deep neural network model configured as a digital twin of the radiologist.

    Interpretable task-specific dimensionality reduction
    28.
    发明授权
    Interpretable task-specific dimensionality reduction 有权

    公开(公告)号：US12249023B2

    公开(公告)日：2025-03-11

    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

    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.

    EXPLAINABLE CONFIDENCE ESTIMATION FOR LANDMARK LOCALIZATION
    29.
    发明申请
    EXPLAINABLE CONFIDENCE ESTIMATION FOR LANDMARK LOCALIZATION 有权

    公开(公告)号：US20250045951A1

    公开(公告)日：2025-02-06

    申请号：US18362224

    申请日：2023-07-31

    Applicant: GE Precision Healthcare LLC

    Inventor： Bipul Das ,  Deepa Anand ,  Vanika Singhal ,  Rakesh Mullick

    IPC: G06T7/73 ,  G06T7/00 ,  G06V10/774 ,  G06V10/82 ,  G06V20/50 ,  G06V20/70 ,  G16H30/40

    Abstract: Systems/techniques that facilitate explainable confidence estimation for landmark localization are provided. In various embodiments, a system can access a three-dimensional voxel array captured by a medical imaging scanner and can localize, via execution of a first deep learning neural network, a set of anatomical landmarks depicted in the three-dimensional voxel array. In various aspects, the system can generate a multi-tiered confidence score collection based on the set of anatomical landmarks and based on a training dataset on which the first deep learning neural network was trained. In various instances, the system can, in response to one or more confidence scores from the multi-tiered confidence score collection failing to satisfy a threshold, generate, via execution of a second deep learning neural network, a classification label that indicates an explanatory factor for why the one or more confidence scores failed to satisfy the threshold.

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