Patents by Inventor Eric C. Poblenz

Eric C. Poblenz 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: 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: 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: 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: 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: 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
  • Publication number: 20200160969
    Abstract: An accession number correction system is operable to determine that an accession number of a received DICOM image does not link to any corresponding one of a plurality of medical reports. A query indicating medical report criteria, generated based on the first DICOM image, is transmitted to a report database, and a set of medical reports are received from the report database in response. One report of the set of medical reports that corresponds to the DICOM image is determined by performing a comparison function on the DICOM image and the one reports to generate a comparison value, and by determining the comparison value compares favorably to a comparison threshold. Updated report header data that includes the accession number of the first DICOM image is generated for the one report and is transmitted to the report database for storage.
    Type: Application
    Filed: March 25, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Eric C. Poblenz, Kevin Lyman, Chris Croswhite
  • 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: 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