公开(公告)号：US12178621B2

公开(公告)日：2024-12-31

申请号：US17667764

申请日：2022-02-09

Applicant: GE Precision Healthcare LLC

Inventor： Sylvain Bernard ,  Vincent Bismuth

IPC: A61B6/02 ,  A61B6/50 ,  G06T7/00 ,  G06T11/00 ,  G16H30/40

Abstract: A convolutional neural network (CNN) is employed in an automated feature and/or anomaly detection system for analyzing images provided by X-ray imaging system. The automated detection system operates in a manner that reduces the number of full-resolution CNN convolution layers required in order to speed up the network inference and learning processes for the detection system. To do so, the detection system utilizes as an input a more compact representation of the tomographic data to alleviate the CNN memory footprint and computation time issues in prior art X-ray systems.

公开(公告)号：US20240144441A1

公开(公告)日：2024-05-02

申请号：US17975899

申请日：2022-10-28

Applicant: GE Precision Healthcare LLC

Inventor： Michel Souheil Tohme ,  German Guillermo Vera Gonzalez ,  Ludovic Boilevin Kayl ,  Vincent Bismuth ,  Tao Tan

IPC: G06T5/00 ,  G06T5/20 ,  G06T7/00

CPC classification number: G06T5/002 ,  G06T5/20 ,  G06T7/0014 ,  A61B6/5258 ,  G06T2200/24 ,  G06T2207/10116 ,  G06T2207/20081 ,  G06T2207/30061

Abstract: Various methods and systems are provided for training a denoising system for a digital imaging system. The denoising system can be a deep learning denoising system formed as a blind or non-blind denoising system in which the training dataset provided to the denoising system includes a noisy image formed with simulated noise added to a clean digital image, and a reference image formed of the clean image having residual noise added thereto, where the residual noise is a fraction of the simulated noise used to form the noisy image. The use of the residual noise within the reference image of the training dataset teaches the DL network in the training process to remove less than all the noise during subsequent inferencing of digital images from the digital imaging system. By leaving selected amounts of noise in the digital images, the denoiser can be tuned to improve image attributes and texture.

公开(公告)号：US20230394296A1

公开(公告)日：2023-12-07

申请号：US17805375

申请日：2022-06-03

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Gopal B. Avinash ,  Ludovic Boilevin Kayl ,  Vincent Bismuth ,  Michel S. Tohme ,  German Guillermo Vera Gonzalez

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Systems/techniques that facilitate improved neural network inferencing efficiency with fewer parameters are provided. In various embodiments, a system can access a medical image on which an artificial intelligence task is to be performed. In various aspects, the system can facilitate the artificial intelligence task by executing a neural network pipeline on the medical image, thereby yielding an artificial intelligence task output that corresponds to the medical image. In various instances, the neural network pipeline can include respective skip connections from the medical image, prior to any convolutions, to each convolutional layer in the neural network pipeline.