Patents by Inventor Hassan Muhammad

Hassan Muhammad 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: 20250125054
    Abstract: Disclosed are systems and methods for identifying prostate cancer patients at high-risk of progression among clinically intermediate risk group. Images of patient cells are obtained and tiled into subsets of smaller images. Using a trained machine learning model, a morphology quantification process is performed on the subsets of smaller images. Portions of the images are input into the trained machine learning models. The trained machine learning model determines the occurrence of likely cancer cells and classifies the cancer cells with a grading. The system then uses this out of the machine learning model to identify whether the cells in the subsets of smaller images indicate whether a prostate cancer patient is at a risk of progression among a clinically intermediate risk group.
    Type: Application
    Filed: October 17, 2024
    Publication date: April 17, 2025
    Inventors: Hassan Muhammad, Chensu Xie, Parag Jain, Rajat Roy, Ashutosh K. Tewari, Dimple Chakravarty, Sujit S. Nair
  • Publication number: 20230298172
    Abstract: Presented herein are systems and methods of clustering images using encoder-decoder models. A computing system may identify tiles derived from an image. Each tile may have a first dimension. The computing system may apply an image reconstruction model to the tiles. The image reconstruction model may include an encoder block having a first set of weights to generate embedding representations corresponding to the tiles. Each embedding representation may have a second dimension lower than the first dimension. The image reconstruction model may include a decoder block having a second set of weights to generate reconstructed tiles corresponding to the embedding representations. The computing system may apply a clustering model comprising a feature space to the embedding representations to classify each tile to one of a plurality of conditions.
    Type: Application
    Filed: March 27, 2023
    Publication date: September 21, 2023
    Applicant: MEMORIAL SLOAN KETTERING CANCER CENTER
    Inventors: Thomas J. Fuchs, Hassan Muhammad
  • Publication number: 20230289955
    Abstract: Presented herein are systems and methods of classifying biomedical images. A computing system may identify a first plurality of tiles from a first biomedical image of a first sample. The computing system may determine a first category for the first sample by applying the plurality of tiles to a classification model. The classification model may include a tile encoder to determine, based on the first plurality of tiles, a corresponding plurality of feature vectors in a feature space. The classification model may include a clusterer to select a subset of feature vectors from the plurality of feature vectors based on a plurality of centroids defined in the feature space. The classification model may include an aggregator to generate, based on the subset of feature vectors, the first category for the sample. The computing system may store an association between the first category and the first biomedical image.
    Type: Application
    Filed: June 2, 2021
    Publication date: September 14, 2023
    Applicant: MEMORIAL SLOAN KETTERING CANCER CENTER
    Inventors: Chensu XIE, Hassan MUHAMMAD, Chad M. VANDERBILT, Raul CASO, Dig Vijay Kumar YARLAGADDA, Gabriele CAMPANELLA, Thomas J. FUCHS
  • Patent number: 11615264
    Abstract: Presented herein are systems and methods of clustering images using encoder-decoder models. A computing system may identify tiles derived from an image. Each tile may have a first dimension. The computing system may apply an image reconstruction model to the tiles. The image reconstruction model may include an encoder block having a first set of weights to generate embedding representations corresponding to the tiles. Each embedding representation may have a second dimension lower than the first dimension. The image reconstruction model may include a decoder block having a second set of weights to generate reconstructed tiles corresponding to the embedding representations. The computing system may apply a clustering model comprising a feature space to the embedding representations to classify each tile to one of a plurality of conditions.
    Type: Grant
    Filed: March 5, 2020
    Date of Patent: March 28, 2023
    Assignee: Memorial Sloan Kettering Cancer Center
    Inventors: Thomas J. Fuchs, Hassan Muhammad
  • Publication number: 20230077504
    Abstract: Presented herein are systems and methods for determining scores from biomedical images. A computing system may identify a plurality of tiles in a first biomedical image derived from a sample of a subject. Each tile may correspond to features of the sample. The computing system may apply the plurality of tiles to a machine learning (ML) model. The ML model may include: an encoder to generate a plurality of feature vectors based on the plurality of tiles; a clusterer to select a subset from the plurality of feature vectors; and an aggregator to determine a first score indicative of a time to an event for the subject resulting from the features of the sample. The model may be trained in accordance with a loss derived from second scores determined for second biomedical images. The computing system may store an association between the score and the first biomedical image.
    Type: Application
    Filed: September 1, 2022
    Publication date: March 16, 2023
    Applicant: MEMORIAL SLOAN KETTERING CANCER CENTER
    Inventors: Hassan Muhammad, Chensu Xie
  • Publication number: 20200285890
    Abstract: Presented herein are systems and methods of clustering images using encoder-decoder models. A computing system may identify tiles derived from an image. Each tile may have a first dimension. The computing system may apply an image reconstruction model to the tiles. The image reconstruction model may include an encoder block having a first set of weights to generate embedding representations corresponding to the tiles. Each embedding representation may have a second dimension lower than the first dimension. The image reconstruction model may include a decoder block having a second set of weights to generate reconstructed tiles corresponding to the embedding representations. The computing system may apply a clustering model comprising a feature space to the embedding representations to classify each tile to one of a plurality of conditions.
    Type: Application
    Filed: March 5, 2020
    Publication date: September 10, 2020
    Applicant: MEMORIAL SLOAN KETTERING CANCER CENTER
    Inventors: Thomas J. Fuchs, Hassan Muhammad