公开(公告)号：US20230041575A1

公开(公告)日：2023-02-09

申请号：US17392431

申请日：2021-08-03

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Buer Qi ,  Dejun Wang ,  Gopal B. Avinash ,  Gireesha Chinthamani Rao ,  German Guillermo Vera Gonzalez ,  Lehel Ferenczi

IPC: G06T11/00 ,  G06N20/00

Abstract: Systems/techniques that facilitate AI-based region-of-interest masks for improved data reconstructions are provided. In various embodiments, a system can access a set of two-dimensional medical scan projections. In various aspects, the system can generate a set of two-dimensional region-of-interest masks respectively corresponding to the set of two-dimensional medical scan projections. In various instances, the system can generate a region-of-interest visualization based on the set of two-dimensional region-of-interest masks and the set of two-dimensional medical scan projections. In various cases, the system can generate the set of two-dimensional region-of-interest masks by executing a machine learning segmentation model on the set of two-dimensional medical scan projections.

公开(公告)号：US20230034782A1

公开(公告)日：2023-02-02

申请号：US17388997

申请日：2021-07-29

Applicant: GE Precision Healthcare LLC

Inventor： Hsi-Ming Chang ,  Li Huazhang ,  Gopal B. Avinash ,  Michael Joseph Washburn ,  Venkata Ratnam Saripalli

IPC: G06N3/08 ,  G06N3/04

Abstract: Systems/techniques that facilitate learning-based clean data selection are provided. In various embodiments, a system can access a raw dataset. In various aspects, the system can select, via execution of a data selection machine learning model, a clean dataset from the raw dataset. In various instances, the system can train a target machine learning model to perform a target task based on the clean dataset. In various aspects, the clean dataset can include candidate-annotation groupings that are in the raw dataset and that are determined by the data selection machine learning model to be suitable for training of the target machine learning model, and the clean dataset can exclude candidate-annotation groupings that are in the raw dataset and that are determined by the data selection machine learning model to not be suitable for training of the target machine learning model.

公开(公告)号：US20210279869A1

公开(公告)日：2021-09-09

申请号：US17327239

申请日：2021-05-21

Applicant: GE Precision Healthcare LLC

Inventor： Ravi Soni ,  Min Zhang ,  Zili Ma ,  Gopal B. Avinash

IPC: G06T7/00 ,  G06N3/08 ,  G16H30/40

Abstract: Techniques are provided for deep neural network (DNN) identification of realistic synthetic images generated using a generative adversarial network (GAN). According to an embodiment, a system is described that can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise, a first extraction component that extracts a subset of synthetic images classified as non-real like as opposed to real-like, wherein the subset of synthetic images were generated using a GAN model. The computer executable components can further comprise a training component that employs the subset of synthetic images and real images to train a DNN network model to classify synthetic images generated using the GAN model as either real-like or non-real like

公开(公告)号：US20210042643A1

公开(公告)日：2021-02-11

申请号：US16944762

申请日：2020-07-31

Applicant: GE Precision Healthcare LLC

Inventor： Junpyo Hong ,  Venkata Ratnam Saripalli ,  Gopal B. Avinash ,  Karley Marty Yoder ,  Keith Bigelow

IPC: G06N5/04 ,  G06N20/00

Abstract: Techniques are described for performing active surveillance and learning for machine learning (ML) model authoring and deployment workflows. In an embodiment, a method comprises applying, by a system comprising a processor, a primary ML model trained on a training dataset to data samples excluded from the training dataset to generate inferences based on the data samples. The method further comprises employing, by the system, one or more active surveillance techniques to regulate performance of the primary ML model in association with the applying, wherein the one or more active surveillance techniques comprise at least one of, performing a model scope evaluation of the primary ML model relative to the data samples or using a domain adapted version of the primary ML model to generate the inferences.

公开(公告)号：US20200311913A1

公开(公告)日：2020-10-01

申请号：US16370082

申请日：2019-03-29

Applicant: GE Precision Healthcare LLC

Inventor： Ravi Soni ,  Min Zhang ,  Zili Ma ,  Gopal B. Avinash

IPC: G06T7/00 ,  G06N3/08 ,  G16H30/40

Abstract: Techniques are provided for deep neural network (DNN) identification of realistic synthetic images generated using a generative adversarial network (GAN). According to an embodiment, a system is described that can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise, a first extraction component that extracts a subset of synthetic images classified as non-real like as opposed to real-like, wherein the subset of synthetic images were generated using a GAN model. The computer executable components can further comprise a training component that employs the subset of synthetic images and real images to train a DNN network model to classify synthetic images generated using the GAN model as either real-like or non-real like