Patents by Inventor Levente Imre Török

Levente Imre Török 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).

  • Publication number: 20230252614
    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.
    Type: Application
    Filed: April 21, 2023
    Publication date: August 10, 2023
    Inventors: Tao Tan, Pál Tegzes, Levente Imre Török, Lehel Ferenczi, Gopal B. Avinash, László Ruskó, Gireesha Chinthamani Rao, Khaled Younis, Soumya Ghose
  • Patent number: 11669945
    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.
    Type: Grant
    Filed: April 27, 2020
    Date of Patent: June 6, 2023
    Assignee: GE PRECISION HEALTHCARE LLC
    Inventors: Tao Tan, Pál Tegzes, Levente Imre Török, Lehel Ferenczi, Gopal B. Avinash, László Ruskó, Gireesha Chinthamani Rao, Khaled Younis, Soumya Ghose
  • Patent number: 11537885
    Abstract: Systems and techniques that facilitate freeze-out as a regularizer in training neural networks are presented. A system can include a memory and a processor that executes computer executable components. The computer executable components can include: an assessment component that identifies units of a neural network, a selection component that selects a subset of units of the neural network, and a freeze-out component that freezes the selected subset of units of the neural network so that weights of output connections from the frozen subset of units will not be updated for a training run.
    Type: Grant
    Filed: January 27, 2020
    Date of Patent: December 27, 2022
    Assignee: GE PRECISION HEALTHCARE LLC
    Inventors: Tao Tan, Min Zhang, Gopal Biligeri Avinash, Lehel Ferenczi, Levente Imre Török, Pál Tegzes
  • Publication number: 20210334598
    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.
    Type: Application
    Filed: April 27, 2020
    Publication date: October 28, 2021
    Inventors: Tao Tan, Pál Tegzes, Levente Imre Török, Lehel Ferenczi, Gopal B. Avinash, László Ruskó, Gireesha Chinthamani Rao, Khaled Younis, Soumya Ghose
  • Publication number: 20210232909
    Abstract: Systems and techniques that facilitate freeze-out as a regularizer in training neural networks are presented. A system can include a memory and a processor that executes computer executable components. The computer executable components can include: an assessment component that identifies units of a neural network, a selection component that selects a subset of units of the neural network, and a freeze-out component that freezes the selected subset of units of the neural network so that weights of output connections from the frozen subset of units will not be updated for a training run.
    Type: Application
    Filed: January 27, 2020
    Publication date: July 29, 2021
    Inventors: Tao Tan, Min Zhang, Gopal Biligeri Avinash, Lehel Ferenczi, Levente Imre Török, Pál Tegzes