Patents by Inventor Diego ARDILA

Diego ARDILA 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).

  • Patent number: 12271443
    Abstract: One embodiment of the present invention sets forth a technique for curating a data sample set. The technique includes determining one or more data sampling criteria based on a sampling objective for a data sample set associated with the machine learning model. The technique also includes selecting, from a set of unlabeled data samples, at least one data sample to be labeled and added to a data sample set associated with the machine learning model based on the one or more data sampling criteria. The technique also includes, for each selected data sample, supplementing the data sample set with the selected data sample and at least one association with a label.
    Type: Grant
    Filed: September 23, 2021
    Date of Patent: April 8, 2025
    Assignee: SCALE AI, INC.
    Inventors: Diego Ardila, Russell Kaplan, Vinjai Saraj Vale, Jihan Yin
  • Patent number: 12032658
    Abstract: A method and system to generate a probabilistic prediction of the presence/absence of cancer in longitudinal and current image datasets, and/or multimodal image datasets, and the location of the cancer, is described. The method and system uses an ensemble of deep learning models. The ensemble includes a global model in the form of a 3D convolutional neural network (CNN) extracting features in the datasets indicative of the presence of cancer on a global basis. The ensemble also includes a two-stage prediction model which includes a first stage or detection model which identifies cancer detection candidates (different cropped volumes of 3D data in the a dataset containing candidates which may be cancer) and a second stage or probability model which incorporates the longitudinal datasets (or multimodal images in a multimodal dataset) and the extracted features from the global model and assigns a cancer probability p to each of the cancer detection candidates.
    Type: Grant
    Filed: November 20, 2018
    Date of Patent: July 9, 2024
    Assignee: Google LLC
    Inventors: Atilla Kiraly, Shravya Shetty, Sujeeth Bharadwaj, Diego Ardila, Bokyung Choi
  • Publication number: 20210225511
    Abstract: A method and system to generate a probabilistic prediction of the presence/absence of cancer in longitudinal and current image datasets, and/or multimodal image datasets, and the location of the cancer, is described. The method and system uses an ensemble of deep learning models. The ensemble includes a global model in the form of a 3D convolutional neural network (CNN) extracting features in the datasets indicative of the presence of cancer on a global basis. The ensemble also includes a two-stage prediction model which includes a first stage or detection model which identifies cancer detection candidates (different cropped volumes of 3D data in the a dataset containing candidates which may be cancer) and a second stage or probability model which incorporates the longitudinal datasets (or multimodal images in a multimodal dataset) and the extracted features from the global model and assigns a cancer probability p to each of the cancer detection candidates.
    Type: Application
    Filed: November 20, 2018
    Publication date: July 22, 2021
    Inventors: Atilla KIRALY, Shravya SHETTY, Sujeeth BHARADWAJ, Diego ARDILA, Bokyung CHOI