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    Interpretable task-specific dimensionality reduction
    11.
    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
    12.
    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.

    DATA CANDIDATE QUERYING VIA EMBEDDINGS FOR DEEP LEARNING REFINEMENT
    13.
    发明申请
    DATA CANDIDATE QUERYING VIA EMBEDDINGS FOR DEEP LEARNING REFINEMENT 有权

    公开(公告)号：US20240379226A1

    公开(公告)日：2024-11-14

    申请号：US18313775

    申请日：2023-05-08

    Applicant: GE Precision Healthcare LLC

    Inventor： Pavan Annangi ,  Deepa Anand

    IPC: G16H50/20 ,  G06N3/088 ,  G06N3/09 ,  G06T7/00 ,  G16H30/20

    Abstract: Systems/techniques that facilitate data candidate querying via embeddings for deep learning refinement are provided. In various embodiments, a system can access a test data candidate provided by a client, generate, via a first deep learning neural network, an inferencing output based on the test data candidate, and access feedback indicating whether the client accepts or rejects the inferencing output. In various aspects, the system can generate, via at least one second deep learning neural network, at least one embedding based on the test data candidate. In various instances, the system can, in response to the feedback indicating that the client rejects the inferencing output, identify, in a candidate-embedding dataset, one or more data candidates whose embeddings are within a threshold level of similarity to the at least one embedding and can retrain the first deep learning neural network based on the one or more data candidates.

    ENSEMBLED QUERYING OF EXAMPLE IMAGES VIA DEEP LEARNING EMBEDDINGS
    15.
    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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