Patents by Inventor Xiaojin Dong

Xiaojin Dong 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: 20220375574
    Abstract: Described are techniques for determining an accurate ground-truth Bayesian Inter-Reviewer Agreement Rate (BIRAR) from unreliable sources. For instance, a process can include obtaining an initial set of diagnostic imaging exams, wherein each diagnostic imaging exam includes a severity grade associated with an initial radiologist. For each diagnostic imaging exam, two or more secondary quality assurance (QA) reviews can be obtained for each diagnostic imaging exam, wherein the secondary QA reviews are associated with QA'ing radiologists different than the initial radiologist. One or more inter-reviewer agreement rates can be determined for the QA'ing radiologists, based on the secondary QA reviews associated with the QA'ing radiologists.
    Type: Application
    Filed: May 4, 2022
    Publication date: November 24, 2022
    Inventors: Tarmo Aijo, Daniel Elgort, Xiaojin Dong, Denis Whelan, Richard Herzog, Murray Becker, Robert Epstein, Irwin Keller, Ron Vianu
  • Patent number: 11423538
    Abstract: For training data pairs comprising training text (a radiological report) and training images (radiological images associated with the radiological report), a first encoder network determines word embeddings for the training text. A concept is generated from the operation of layers of the first encoder network, which is regularized by a first loss between the generated concept and a labeled concept for the training text. A second encoder network determines features for the training image. A heatmap is generated from the operation of layers of the second encoder network, which is regularized by a second loss between the generated heatmap and a labeled heatmap for the training image. A categorical cross entropy loss is calculated between a diagnostic quality category (classified by an error encoder) and a labeled diagnostic quality category for the training data pair. A total loss function comprising the first, second, and categorical cross entropy losses is minimized.
    Type: Grant
    Filed: April 15, 2020
    Date of Patent: August 23, 2022
    Assignee: Covera Health
    Inventors: Ron Vianu, Tarmo Henrik Aijo, James Robert Browning, Xiaojin Dong, Bryce Eron Eakin, Daniel Robert Elgort, Richard J. Herzog, Benjamin L. Odry, JinHyeong Park, Benjamin Sellman Suutari, Gregory Allen Dubbin
  • Publication number: 20200334809
    Abstract: For training data pairs comprising training text (a radiological report) and training images (radiological images associated with the radiological report), a first encoder network determines word embeddings for the training text. A concept is generated from the operation of layers of the first encoder network, which is regularized by a first loss between the generated concept and a labeled concept for the training text. A second encoder network determines features for the training image. A heatmap is generated from the operation of layers of the second encoder network, which is regularized by a second loss between the generated heatmap and a labeled heatmap for the training image. A categorical cross entropy loss is calculated between a diagnostic quality category (classified by an error encoder) and a labeled diagnostic quality category for the training data pair. A total loss function comprising the first, second, and categorical cross entropy losses is minimized.
    Type: Application
    Filed: April 15, 2020
    Publication date: October 22, 2020
    Inventors: Ron Vianu, Tarmo Henrik Aijo, James Robert Browning, Xiaojin Dong, Bryce Eron Eakin, Daniel Robert Elgort, Richard J. Herzog, Benjamin L. Odry, JinHyeong Park, Benjamin Sellman Suutari, Gregory Allen Dubbin