Patents by Inventor Alexander Risman

Alexander Risman 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).

  • Anomaly detection in volumetric medical images using sequential convolutional and recurrent neural networks
    Patent number: 10417788
    Abstract: Computer-implemented methods and apparatuses for anomaly detection in volumetric images are provided. A two-dimensional convolutional neural network (CNN) is used to encode slices within a volumetric image, such as a CT scan. The CNN may be trained using an output layer that is subsequently omitted during use of the CNN as an encoder. The CNN encoder output is applied to a recurrent neural network (RNN), such as a long short-term memory network. The RNN may output various indications of the presence, probability and/or location of anomalies within the volumetric image.
    Type: Grant
    Filed: September 20, 2017
    Date of Patent: September 17, 2019
    Assignee: REALIZE, INC.
    Inventors: Alexander Risman, Sea Chen
  • Anomaly detection in volumetric images using sequential convolutional and recurrent neural networks
    Patent number: 10347010
    Abstract: Computer-implemented methods and apparatuses for anomaly detection in volumetric images are provided. A two-dimensional convolutional neural network (CNN) is used to encode slices within a volumetric image, such as a CT scan. The CNN may be trained using an output layer that is subsequently omitted during use of the CNN as an encoder. The CNN encoder output is applied to a recurrent neural network (RNN), such as a long short-term memory network. The RNN may output various indications of the presence, probability and/or location of anomalies within the volumetric image.
    Type: Grant
    Filed: September 26, 2017
    Date of Patent: July 9, 2019
    Inventors: Alexander Risman, Sea Chen
  • ANOMALY DETECTION IN VOLUMETRIC IMAGES
    Publication number: 20180082443
    Abstract: Computer-implemented methods and apparatuses for anomaly detection in volumetric images are provided. A two-dimensional convolutional neural network (CNN) is used to encode slices within a volumetric image, such as a CT scan. The CNN may be trained using an output layer that is subsequently omitted during use of the CNN as an encoder. The CNN encoder output is applied to a recurrent neural network (RNN), such as a long short-term memory network. The RNN may output various indications of the presence, probability and/or location of anomalies within the volumetric image.
    Type: Application
    Filed: September 20, 2017
    Publication date: March 22, 2018
    Inventors: Alexander Risman, Sea Chen
  • ANOMALY DETECTION IN VOLUMETRIC IMAGES
    Publication number: 20180033144
    Abstract: Computer-implemented methods and apparatuses for anomaly detection in volumetric images are provided. A two-dimensional convolutional neural network (CNN) is used to encode slices within a volumetric image, such as a CT scan. The CNN may be trained using an output layer that is subsequently omitted during use of the CNN as an encoder. The CNN encoder output is applied to a recurrent neural network (RNN), such as a long short-term memory network. The RNN may output various indications of the presence, probability and/or location of anomalies within the volumetric image.
    Type: Application
    Filed: September 26, 2017
    Publication date: February 1, 2018
    Inventors: Alexander Risman, Sea Chen