公开(公告)号：US20230342427A1

公开(公告)日：2023-10-26

申请号：US18343266

申请日：2023-06-28

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Gopal B. Avinash ,  Máté Fejes ,  Ravi Soni ,  Dániel Attila Szabó ,  Rakesh Mullick ,  Vikram Melapudi ,  Krishna Seetharam Shriram ,  Sohan Rashmi Ranjan ,  Bipul Das ,  Utkarsh Agrawal ,  László Ruskó ,  Zita Herczeg ,  Barbara Darázs

IPC: G06F18/214 ,  G06N5/04 ,  G16H30/40 ,  A61B5/055 ,  G06T5/50 ,  G06F18/21 ,  G06T7/30 ,  A61B5/00 ,  G16H30/20 ,  G16H50/20 ,  G16H50/50 ,  A61B6/03 ,  G06F18/22 ,  G06F18/28 ,  A61B6/00

CPC classification number: G06F18/214 ,  A61B5/055 ,  A61B5/7267 ,  A61B6/032 ,  A61B6/5223 ,  G06F18/2178 ,  G06F18/22 ,  G06F18/28 ,  G06N5/04 ,  G06T5/50 ,  G06T7/30 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/10116 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20212 ,  G06T2207/30004 ,  G06V2201/03

Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

公开(公告)号：US20230284986A1

公开(公告)日：2023-09-14

申请号：US17690258

申请日：2022-03-09

Applicant: GE Precision Healthcare LLC

Inventor： Dejun Wang ,  Buer Qi ,  Tao Tan ,  Gireesha Chinthamani Rao ,  Gopal B. Avinash ,  Qingming Peng ,  Yaan Ge ,  Sylvain Bernard ,  Vincent Bismuth

IPC: A61B6/00 ,  A61B6/02 ,  G06T3/40 ,  G06V10/25 ,  G06N3/04

CPC classification number: A61B6/4429 ,  A61B6/5205 ,  A61B6/025 ,  G06T3/4046 ,  G06V10/25 ,  G06N3/0454

Abstract: A tomosynthesis machine allows for faster image acquisition and improved signal-to-noise by acquiring a projection attenuation data and using machine learning to identify a subset of the projection attenuation data for the production of thinner slices and/or higher resolution slices using machine learning.

公开(公告)号：US11669945B2

公开(公告)日：2023-06-06

申请号：US16858862

申请日：2020-04-27

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Pál Tegzes ,  Levente Imre Török ,  Lehel Ferenczi ,  Gopal B. Avinash ,  László Ruskó ,  Gireesha Chinthamani Rao ,  Khaled Younis ,  Soumya Ghose

IPC: G06F18/21 ,  G06V10/772 ,  G06V10/774 ,  G06V10/762 ,  G06V10/74 ,  G06V10/776 ,  G06T7/00 ,  G06V10/82 ,  G06F18/22 ,  G06F18/23 ,  G06F18/28 ,  G06F18/214

CPC classification number: G06F18/217 ,  G06F18/214 ,  G06F18/22 ,  G06F18/23 ,  G06F18/28 ,  G06T7/00 ,  G06V10/761 ,  G06V10/762 ,  G06V10/772 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

Abstract: Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmonizing the sub-images with corresponding reference sub-images of at least one reference image based on two or more different statistical values respectively calculated for the sub-images and the corresponding reference-sub images, resulting in transformation of the sub-images into modified sub-images images. In some implementations, the modified sub-images can be combined into a harmonized image having a more similar appearance to the at least one reference image relative to the input image. In other implementations, harmonized images and/or modified sub-images generated using these techniques can be used as ground-truth training samples for training one or more deep learning model to transform input images with appearance variations into harmonized images.

公开(公告)号：US20220101048A1

公开(公告)日：2022-03-31

申请号：US17093960

申请日：2020-11-10

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Gopal B. Avinash ,  Máté Fejes ,  Ravi Soni ,  Dániel Attila Szabó ,  Rakesh Mullick ,  Vikram Melapudi ,  Krishna Seetharam Shriram ,  Sohan Rashmi Ranjan ,  Bipul Das ,  Utkarsh Agrawal ,  László Ruskó ,  Zita Herczeg ,  Barbara Darázs

IPC: G06K9/62 ,  G06T5/50 ,  G06T7/30 ,  G06N5/04 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  A61B6/03 ,  A61B6/00 ,  A61B5/055 ,  A61B5/00

Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

公开(公告)号：US20210166351A1

公开(公告)日：2021-06-03

申请号：US16699368

申请日：2019-11-29

Applicant: GE Precision Healthcare, LLC

Inventor： Khaled Salem Younis ,  Katelyn Rose Nye ,  Gireesha Chinthamani Rao ,  German Guillermo Vera Gonzalez ,  Gopal B. Avinash ,  Ravi Soni ,  Teri Lynn Fischer ,  John Michael Sabol

IPC: G06T3/60 ,  G06K9/62 ,  G06T7/00 ,  G06N3/04 ,  G06N3/08

Abstract: An x-ray image orientation detection and correction system including a detection and correction computing device is provided. The processor of the computing device is programmed to execute a neural network model that is trained with training x-ray images as inputs and observed x-ray images as outputs. The observed x-ray images are the training x-ray images adjusted to have a reference orientation. The processor is further programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign an orientation class to the unclassified x-ray image. If the assigned orientation class is not the reference orientation, the processor is programmed to adjust an orientation of the unclassified x-ray image using the neural network model, and output a corrected x-ray image. If the assigned orientation class is the reference orientation, the processor is programmed to output the unclassified x-ray image.

公开(公告)号：US20200342968A1

公开(公告)日：2020-10-29

申请号：US16656034

申请日：2019-10-17

Applicant: GE Precision Healthcare LLC

Inventor： Gopal B. Avinash ,  Qian Zhao ,  Zili Ma ,  Dibyajyoti Pati ,  Venkata Ratnam Saripalli ,  Ravi Soni ,  Jiahui Guan ,  Min Zhang

IPC: G16H15/00 ,  G16H40/67 ,  G06F9/451 ,  G06N20/00 ,  G16H10/60

Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data processing are disclosed. An example apparatus includes a data processor to process one-dimensional data captured over time with respect to patient(s). The example apparatus includes a visualization processor to transform the processed data into graphical representations and to cluster the graphical representations including the first graphical representation into at least first and second blocks arranged with respect to an indicator of a criterion to provide a visual comparison of the first block and the second block with respect to the criterion. The example apparatus includes an interaction processor to facilitate interaction, via the graphical user interface, with the first and second blocks of graphical representations to extract a data set for processing from at least a subset of the first and second blocks.

公开(公告)号：US20200272905A1

公开(公告)日：2020-08-27

申请号：US16450474

申请日：2019-06-24

Applicant: GE Precision Healthcare LLC

Inventor： Venkata Ratnam Saripalli ,  Ravi Soni ,  Jiahui Guan ,  Gopal B. Avinash

IPC: G06N3/08 ,  H03M7/30

Abstract: Systems and computer-implemented methods for facilitating automated compression of artificial neural networks using an iterative hybrid reinforcement learning approach are provided. In various embodiments, a compression architecture can receive as input an original neural network to be compressed. The architecture can perform one or more compression actions to compress the original neural network into a compressed neural network. The architecture can then generate a reward signal quantifying how well the original neural network was compressed. In (α)-proportion of compression iterations/episodes, where α∈[0,1], the reward signal can be computed in model-free fashion based on a compression ratio and accuracy ratio of the compressed neural network. In (1−α)-proportion of compression iterations/episodes, the reward signal can be predicted in model-based fashion using a compression model learned/trained on the reward signals computed in model-free fashion. This hybrid model-free-and-model-based architecture can greatly reduce convergence time without sacrificing substantial accuracy.

公开(公告)号：US12236593B2

公开(公告)日：2025-02-25

申请号：US17975867

申请日：2022-10-28

Applicant: GE Precision Healthcare LLC

Inventor： Gireesha Chinthamani Rao ,  Ravi Soni ,  Gopal B. Avinash ,  Poonam Dalal ,  Beth A. Heckel

IPC: G06T7/70 ,  G06T3/60 ,  G06T7/00

Abstract: An artificial intelligence (AI) X-ray image information detection and correction system is employed either as a component of the X-ray imaging system or separately from the X-ray imaging system to automatically scan post-exposure X-ray images to detect various types of information or characteristics of the X-ray image, including, but not limited to, anatomy, view, orientation and laterality of the X-ray image, along with an anatomical landmark segmentation. The information detected about the X-ray image can then be stored by the AI system in association with the X-ray image for use in various downstream X-ray system workflow automations and/or reviews of the X-ray image.

公开(公告)号：US12159420B2

公开(公告)日：2024-12-03

申请号：US17457179

申请日：2021-12-01

Applicant: GE Precision Healthcare LLC

Inventor： Dibyajyoti Pati ,  Junpyo Hong ,  Venkata Ratnam Saripalli ,  German Guillermo Vera Gonzalez ,  Dejun Wang ,  Aizhen Zhou ,  Gopal B. Avinash ,  Ravi Soni ,  Tao Tan ,  Fuqiang Chen ,  Yaan Ge

IPC: G06T7/30 ,  A61B6/00 ,  G06N3/08

Abstract: Various methods and systems are provided for automatically registering and stitching images. In one example, a method includes entering a first image of a subject and a second image of the subject to a model trained to output a transformation matrix based on the first image and the second image, where the model is trained with a plurality of training data sets, each training data set including a pair of images, a mask indicating a region of interest (ROI), and associated ground truth, automatically stitching together the first image and the second image based on the transformation matrix to form a stitched image, and outputting the stitched image for display on a display device and/or storing the stitched image in memory.

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.