Patents by Inventor Chenyu You

Chenyu You has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • CT super-resolution GAN constrained by the identical, residual and cycle learning ensemble (GAN-circle)
    Patent number: 11854160
    Abstract: A system for generating a high resolution (HR) computed tomography (CT) image from a low resolution (LR) CT image is described. The system includes a first generative adversarial network (GAN) and a second GAN. The first GAN includes a first generative neural network (G) configured to receive a training LR image dataset and to generate a corresponding estimated HR image dataset, and a first discriminative neural network (DY) configured to compare a training HR image dataset and the estimated HR image dataset. The second GAN includes a second generative neural network (F) configured to receive the training HR image dataset and to generate a corresponding estimated LR image dataset, and a second discriminative neural network (DX) configured to compare the training LR image dataset and the estimated LR image dataset.
    Type: Grant
    Filed: December 29, 2021
    Date of Patent: December 26, 2023
    Assignee: Rensselaer Polytechnic Institute
    Inventors: Ge Wang, Chenyu You, Wenxiang Cong, Hongming Shan
  • CT SUPER-RESOLUTION GAN CONSTRAINED BY THE IDENTICAL, RESIDUAL AND CYCLE LEARNING ENSEMBLE (GAN-CIRCLE)
    Publication number: 20220230278
    Abstract: A system for generating a high resolution (HR) computed tomography (CT) image from a low resolution (LR) CT image is described. The system includes a first generative adversarial network (GAN) and a second GAN. The first GAN includes a first generative neural network (G) configured to receive a training LR image dataset and to generate a corresponding estimated HR image dataset, and a first discriminative neural network (DY) configured to compare a training HR image dataset and the estimated HR image dataset. The second GAN includes a second generative neural network (F) configured to receive the training HR image dataset and to generate a corresponding estimated LR image dataset, and a second discriminative neural network (DX) configured to compare the training LR image dataset and the estimated LR image dataset.
    Type: Application
    Filed: December 29, 2021
    Publication date: July 21, 2022
    Applicant: RENSSELAER POLYTECHNIC INSTITUTE
    Inventors: Ge Wang, Chenyu You, Wenxiang Cong, Hongming Shan
  • CT super-resolution GAN constrained by the identical, residual and cycle learning ensemble (GAN-circle)
    Patent number: 11232541
    Abstract: A system for generating a high resolution (HR) computed tomography (CT) image from a low resolution (LR) CT image is described. The system includes a first generative adversarial network (GAN) and a second GAN. The first GAN includes a first generative neural network (G) configured to receive a training LR image dataset and to generate a corresponding estimated HR image dataset, and a first discriminative neural network (DY) configured to compare a training HR image dataset and the estimated HR image dataset. The second GAN includes a second generative neural network (F) configured to receive the training HR image dataset and to generate a corresponding estimated LR image dataset, and a second discriminative neural network (DX) configured to compare the training LR image dataset and the estimated LR image dataset.
    Type: Grant
    Filed: October 7, 2019
    Date of Patent: January 25, 2022
    Assignee: Rensselaer Polytechnic Institute
    Inventors: Ge Wang, Chenyu You, Wenxiang Cong, Hongming Shan, Guang Li
  • CT SUPER-RESOLUTION GAN CONSTRAINED BY THE IDENTICAL, RESIDUAL AND CYCLE LEARNING ENSEMBLE (GAN-CIRCLE)
    Publication number: 20200111194
    Abstract: A system for generating a high resolution (HR) computed tomography (CT) image from a low resolution (LR) CT image is described. The system includes a first generative adversarial network (GAN) and a second GAN. The first GAN includes a first generative neural network (G) configured to receive a training LR image dataset and to generate a corresponding estimated HR image dataset, and a first discriminative neural network (DY) configured to compare a training HR image dataset and the estimated HR image dataset. The second GAN includes a second generative neural network (F) configured to receive the training HR image dataset and to generate a corresponding estimated LR image dataset, and a second discriminative neural network (DX) configured to compare the training LR image dataset and the estimated LR image dataset.
    Type: Application
    Filed: October 7, 2019
    Publication date: April 9, 2020
    Applicant: RENSSELAER POLYTECHNIC INSTITUTE
    Inventors: Ge Wang, Chenyu You, Wenxiang Cong, Hongming Shan, Guang Li