Patents by Inventor Yeongmo KIM

Yeongmo KIM 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).

  • Apparatus and method for generating medical image segmentation deep-learning model, and medical image segmentation deep-learning model generated therefrom
    Patent number: 11321589
    Abstract: There is provided a medical image segmentation deep-learning model generation apparatus including a training data generation/allocation unit configured to generate a training dataset through a segmentation result value acquired by inputting a given medical image to an original medical image segmentation deep-learning model and a learning control unit configured to acquire temporary weights using output data corresponding to primary learning by inputting good task data and bad task data sampled from primary learning training datasets to the medical image segmentation deep-learning model and configured to update weights by adding gradients acquired using weights acquired using output data corresponding to secondary learning by inputting good task data and bad task data sampled from secondary learning training datasets to the medical image segmentation deep-learning model, wherein the primary learning and the secondary learning are repeated.
    Type: Grant
    Filed: December 6, 2019
    Date of Patent: May 3, 2022
    Assignees: Seoul National University R&DB Foundation, hodooAI Lab Inc.
    Inventors: Jungwoo Lee, Sungyeob Han, Yeongmo Kim, Seokhyeon Ha
  • APPARATUS AND METHOD FOR GENERATING MEDICAL IMAGE SEGMENTATION DEEP-LEARNING MODEL, AND MEDICAL IMAGE SEGMENTATION DEEP-LEARNING MODEL GENERATED THEREFROM
    Publication number: 20200184274
    Abstract: There is provided a medical image segmentation deep-learning model generation apparatus including a training data generation/allocation unit configured to generate a training dataset through a segmentation result value acquired by inputting a given medical image to an original medical image segmentation deep-learning model and a learning control unit configured to acquire temporary weights using output data corresponding to primary learning by inputting good task data and bad task data sampled from primary learning training datasets to the medical image segmentation deep-learning model and configured to update weights by adding gradients acquired using weights acquired using output data corresponding to secondary learning by inputting good task data and bad task data sampled from secondary learning training datasets to the medical image segmentation deep-learning model, wherein the primary learning and the secondary learning are repeated.
    Type: Application
    Filed: December 6, 2019
    Publication date: June 11, 2020
    Applicants: Seoul National University R&DB Foundation, hodooAI Lab Inc.
    Inventors: Jungwoo LEE, Sungyeob HAN, Yeongmo KIM, Seokhyeon HA