Patents by Inventor Jordan Prosky

Jordan Prosky 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: 11056220
    Abstract: An intensity transform augmentation system is operable to generate a plurality of sets of augmented images by performing a set of intensity transformation functions on each of a training set of medical scans. Each of the set of intensity transformation functions are based on density properties of corresponding anatomy feature present in the training set of medical scans. A computer vision model is generated by performing a training step on the plurality of sets of augmented images, where each augmented image of a set of augmented images is assigned same output label data based on a corresponding one of the training set of medical scans. Inference data is generated by performing an inference function on a new medical scan by utilizing the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.
    Type: Grant
    Filed: March 21, 2019
    Date of Patent: July 6, 2021
    Assignee: Enlitic, Inc.
    Inventors: Kevin Lyman, Li Yao, Eric C. Poblenz, Jordan Prosky, Ben Covington, Anthony Upton
  • Publication number: 20210183485
    Abstract: A global multi-label generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Global probability data that includes a set of global probability values each indicating a probability that a corresponding one of the set of abnormality classes is present in the new medical scan is generated based on the probability matrix data for transmission to a client device.
    Type: Application
    Filed: February 2, 2021
    Publication date: June 17, 2021
    Applicant: Enlitic, Inc.
    Inventors: Li Yao, Jordan Prosky, Eric C. Poblenz, Kevin Lyman
  • Publication number: 20210082547
    Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.
    Type: Application
    Filed: September 16, 2020
    Publication date: March 18, 2021
    Applicant: Enlitic, Inc.
    Inventors: Li Yao, Jordan Prosky, Eric C. Poblenz, Kevin Lyman, Lionel Lints, Ben Covington, Anthony Upton
  • Publication number: 20210074394
    Abstract: An intensity transform augmentation system is operable to receive a training set of medical scans. Random intensity transformation function parameters are generated for each medical scan of the training set of medical scans. A plurality of augmented images are generated, where each of the plurality of augmented images is generated by performing a intensity transformation function on one of the training set of medical scans by utilizing the random intensity transform parameters generated for the one of the training set of medical scan. A computer vision model is generated by performing a training step on the plurality of augmented images. A new medical scan is received via the receiver. Inference data is generated by performing an inference function that utilizes the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.
    Type: Application
    Filed: November 20, 2020
    Publication date: March 11, 2021
    Applicant: Enlitic, Inc.
    Inventors: Jordan Prosky, Li Yao, Eric C. Poblenz, Kevin Lyman, Ben Covington, Anthony Upton
  • Patent number: 10943681
    Abstract: A global multi-label generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Global probability data that includes a set of global probability values each indicating a probability that a corresponding one of the set of abnormality classes is present in the new medical scan is generated based on the probability matrix data for transmission to a client device.
    Type: Grant
    Filed: March 12, 2019
    Date of Patent: March 9, 2021
    Assignee: Enlitic, Inc.
    Inventors: Li Yao, Jordan Prosky, Eric C. Poblenz, Kevin Lyman
  • Patent number: 10878949
    Abstract: An intensity transform augmentation system is operable to receive a training set of medical scans. Random intensity transformation function parameters are generated for each medical scan of the training set of medical scans. A plurality of augmented images are generated, where each of the plurality of augmented images is generated by performing a intensity transformation function on one of the training set of medical scans by utilizing the random intensity transform parameters generated for the one of the training set of medical scan. A computer vision model is generated by performing a training step on the plurality of augmented images. A new medical scan is received via the receiver. Inference data is generated by performing an inference function that utilizes the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.
    Type: Grant
    Filed: March 21, 2019
    Date of Patent: December 29, 2020
    Assignee: Enlitic, Inc.
    Inventors: Jordan Prosky, Li Yao, Eric C. Poblenz, Kevin Lyman, Ben Covington, Anthony Upton
  • Patent number: 10818386
    Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.
    Type: Grant
    Filed: March 12, 2019
    Date of Patent: October 27, 2020
    Assignee: Enlitic, Inc.
    Inventors: Li Yao, Jordan Prosky, Eric C. Poblenz, Kevin Lyman, Lionel Lints, Ben Covington, Anthony Upton
  • Publication number: 20200160971
    Abstract: A multi-model medical scan analysis system is operable to generate a plurality of training sets from a plurality of medical scans. Each of a set of sub-models can be generated by performing a training step on a corresponding one of the plurality of training sets. A subset of the set of sub-models is selected for a new medical scan. A set of abnormality data is generated by applying a subset of a set of inference functions on the new medical scan, where the subset of the set of inference functions utilize the subset of the set of sub-models. Final abnormality data is generated by performing a final inference function on the set of abnormality data. The final abnormality data can be to a client device for display via a display device.
    Type: Application
    Filed: March 27, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Li Yao, Eric C. Poblenz, Jordan Prosky, Ben Covington, Anthony Upton
  • Publication number: 20200160979
    Abstract: A model-assisted annotating system is operable to receive a first set of annotation data for a first set of medical scans from a set of client devices. A computer vision model is trained by utilizing first set of medical scans and the first set of annotation data. A second set of annotation data for a second set of medical scans is generated by utilizing the computer vision model. The second set of medical scans and the second set of annotation data is transmitted to the set of client devices, and a set of additional annotation data is received in response. An updated computer vision model is generated by utilizing the set of additional annotation data. A third set of annotation data is generated for a third set of medical scans by utilizing the updated computer vision model for transmission to the set of client devices for display.
    Type: Application
    Filed: March 27, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Li Yao, Eric C. Poblenz, Jordan Prosky, Ben Covington, Anthony Upton, Lionel Lints
  • Publication number: 20200160520
    Abstract: A multi-model medical scan analysis system is operable to generate a generic model by performing a training step on image data of a plurality of medical scans and corresponding labeling data. A plurality of fine-tuned models corresponding to one of a plurality of abnormality types can be generated by performing a fine-tuning step on the generic model. Abnormality detection data can be generated for a new medical scan by performing utilizing the generic model. One of the plurality of abnormality types is determined to be detected in the new medical scan based on the abnormality detection data, and a fine-tuned model that corresponds to the abnormality type is selected. Additional abnormality data is generated for the new medical scan by utilizing the selected fine-tuned model. The additional abnormality data can be transmitted to a client device for display via a display device.
    Type: Application
    Filed: March 27, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Jordan Prosky, Li Yao, Eric C. Poblenz, Kevin Lyman, Ben Covington, Anthony Upton
  • Publication number: 20200160983
    Abstract: A medical scan triaging system is operable to generate a global abnormality probability for each of a plurality of medical scans by utilizing a computer vision model trained on a training set of medical scans. A triage probability threshold is determined based on user input to a client device. A first subset of the plurality of medical scans, designated for human review, is determined by identifying medical scans with a corresponding global abnormality probability that compares favorably to the triage probability threshold. A second subset of the plurality of medical scans, designated as normal, is determined by identifying ones of the plurality of medical scans with a corresponding global abnormality probability that compares unfavorably to the triage probability threshold. Transmission of the first subset of the plurality of medical scans to a plurality of client devices associated with a plurality of users is facilitated.
    Type: Application
    Filed: March 27, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Li Yao, Eric C. Poblenz, Jordan Prosky, Ben Covington, Anthony Upton
  • Publication number: 20200160977
    Abstract: An intensity transform augmentation system is operable to generate a plurality of sets of augmented images by performing a set of intensity transformation functions on each of a training set of medical scans. Each of the set of intensity transformation functions are based on density properties of corresponding anatomy feature present in the training set of medical scans. A computer vision model is generated by performing a training step on the plurality of sets of augmented images, where each augmented image of a set of augmented images is assigned same output label data based on a corresponding one of the training set of medical scans. Inference data is generated by performing an inference function on a new medical scan by utilizing the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.
    Type: Application
    Filed: March 21, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Li Yao, Eric C. Poblenz, Jordan Prosky, Ben Covington, Anthony Upton
  • Publication number: 20200160974
    Abstract: A global multi-label generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Global probability data that includes a set of global probability values each indicating a probability that a corresponding one of the set of abnormality classes is present in the new medical scan is generated based on the probability matrix data for transmission to a client device.
    Type: Application
    Filed: March 12, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Li Yao, Jordan Prosky, Eric C. Poblenz, Kevin Lyman
  • Publication number: 20200160544
    Abstract: A contrast parameter learning system is operable to generate contrast significance data for a computer vision model, where the computer vision model was generated by performing a training step on a training set of medical scans. Significant contrast parameters are identified based on the contrast significance data. A re-contrasted training set is generated by performing an intensity transformation function that utilizes the significant contrast parameters on the training set of medical scans. A re-trained model is generated by performing the training step on the first re-contrasted training set. Re-contrasted image data of a new medical scan is generated by performing the intensity transformation function. Inference data is generated by performing an inference function that utilizes the first re-trained model on the re-contrasted image data. The inference data is transmitted via the transmitter to a client device for display via a display device.
    Type: Application
    Filed: March 21, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Li Yao, Jordan Prosky, Eric C. Poblenz, Kevin Lyman, Ben Covington, Anthony Upton
  • Publication number: 20200160975
    Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Heat map visualization data can be generated for transmission to a client device based on the probability matrix data that indicates, for each of the set of abnormality classes, a color value for each pixel of the new medical scan.
    Type: Application
    Filed: March 12, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Li Yao, Jordan Prosky, Eric C. Poblenz, Kevin Lyman, Lionel Lints, Ben Covington, Anthony Upton
  • Publication number: 20200160978
    Abstract: An intensity transform augmentation system is operable to receive a training set of medical scans. Random intensity transformation function parameters are generated for each medical scan of the training set of medical scans. A plurality of augmented images are generated, where each of the plurality of augmented images is generated by performing a intensity transformation function on one of the training set of medical scans by utilizing the random intensity transform parameters generated for the one of the training set of medical scan. A computer vision model is generated by performing a training step on the plurality of augmented images. A new medical scan is received via the receiver. Inference data is generated by performing an inference function that utilizes the computer vision model on the new medical scan. The inference data is transmitted to a client device for display via a display device.
    Type: Application
    Filed: March 21, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Jordan Prosky, Li Yao, Eric C. Poblenz, Kevin Lyman, Ben Covington, Anthony Upton
  • Publication number: 20200160970
    Abstract: A medical scan header standardization system is operable to determine a set of standard DICOM headers based on determining a standard set of fields and based on further determining a standard set of entries for each of the standard set of fields. A DICOM image is received via a network, and a header of the DICOM image is determined to be incorrect. A selected one of the set of standard DICOM headers to replace the header of the DICOM image is determined. The selected one of the set of standard DICOM headers is transmitted, via the network, to a medical scan database for storage in conjunction with the DICOM image.
    Type: Application
    Filed: March 25, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Anthony Upton, Li Yao, Jordan Prosky, Eric C. Poblenz, Chris Croswhite, Ben Covington
  • Publication number: 20200161005
    Abstract: A location-based medical scan analysis system is operable to generate a generic model by performing a training step on image data of a plurality of medical scans. Location-based subsets of the plurality of medical scans are generated by including ones of the plurality of medical scans with originating locations that compare favorably to location grouping criteria for the each location-based subset. A plurality of location-based models are generated by performing a fine-tuning step on the generic model, utilizing a corresponding one of the plurality of location-based subsets. Inference data is generated for a new medical scan by utilizing one of the location-based models on the new medical scan, where an originating location associated with the new medical scan compares favorably to location grouping criteria for the location-based subset utilized to generate the location-based model. The inference data is transmitted to a client device for display via a display device.
    Type: Application
    Filed: March 27, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Li Yao, Eric C. Poblenz, Jordan Prosky, Ben Covington, Anthony Upton