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).

  • 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: 20210110900
    Abstract: The de-identification system can be operable to receive, from at least one first entity, a medical scan and a corresponding medical report. A set of patient identifiers can be identified in a subset of fields of a header of the medical scan. A de-identified medical scan can be generated by replacing the subset of fields of the header of the medical scan with a corresponding set of anonymized fields generated by performing a header anonymization function. A subset of patient identifiers of the set of patient identifiers can be identified in the medical report. A de-identified medical report can be generated by replacing each of the subset of patient identifiers with corresponding anonymized placeholder text generated by performing a text anonymization function on the subset of patient identifiers. The de-identified medical scan and the de-identified medical report can be transmitted to a second entity via a network.
    Type: Application
    Filed: December 22, 2020
    Publication date: April 15, 2021
    Applicant: Enlitic, Inc.
    Inventors: Eric C. Poblenz, Kevin Lyman, Chris Croswhite
  • 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: 10916337
    Abstract: The de-identification system can be operable to receive, from at least one first entity, a medical scan and a corresponding medical report. A set of patient identifiers can be identified in a subset of fields of a header of the medical scan. A de-identified medical scan can be generated by replacing the subset of fields of the header of the medical scan with a corresponding set of anonymized fields generated by performing a header anonymization function. A subset of patient identifiers of the set of patient identifiers can be identified in the medical report. A de-identified medical report can be generated by replacing each of the subset of patient identifiers with corresponding anonymized placeholder text generated by performing a text anonymization function on the subset of patient identifiers. The de-identified medical scan and the de-identified medical report can be transmitted to a second entity via a network.
    Type: Grant
    Filed: March 13, 2019
    Date of Patent: February 9, 2021
    Assignee: Enlitic, Inc.
    Inventors: Eric C. Poblenz, Kevin Lyman, Chris Croswhite
  • 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
  • Publication number: 20200373003
    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; generate comparison data by comparing the global abnormality probability for each of the plurality of medical scans to a triage probability threshold; sort the plurality of medical scans, based on the comparison data, into a first subset of the plurality of medical scans, each having one of the plurality of abnormalities present, and a second subset of the plurality of medical scans, each having all of the plurality of abnormalities absent; and facilitate transmission, based on the sorting, of the first subset of the plurality of medical scans and the second subset of the plurality of medical scans to at least one medical scan viewing system.
    Type: Application
    Filed: August 12, 2020
    Publication date: November 26, 2020
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Li Yao, Eric C. Poblenz, Ben Covington, Anthony Upton
  • Publication number: 20200357117
    Abstract: A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of medical 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 medical 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. Preliminary heat map visualization data can be generated for transmission to a client device based on the probability matrix data. Heat map visualization data can be generated via a post-processing of the preliminary heat map visualization data to mitigate heat map artifacts.
    Type: Application
    Filed: July 27, 2020
    Publication date: November 12, 2020
    Applicant: Enlitic, Inc.
    Inventors: Kevin Lyman, Eric C. Poblenz, Li Yao, Ben Covington, Anthony Upton
  • Publication number: 20200357118
    Abstract: A multi-label generating system is configured to: store a first plurality of medical scans with corresponding global labels and a second plurality of medical scans with corresponding region labels, wherein the global labels each correspond to one of a set of abnormality classes and wherein each of the region labels correspond to one of the set of abnormality classes; generate a computer vision model by training on the first plurality of medical scans with the corresponding global labels and the second plurality of medical scans with the corresponding region labels; receive a new medical scan; generate global probability data based on the computer vision model, wherein the global probability data indicates a set of global probability values corresponding to the set of abnormality classes, and wherein each of the set of global probability values indicates a probability that a corresponding one of the set of abnormality classes is present in the new medical scan; and transmit the global probability data to a cli
    Type: Application
    Filed: July 29, 2020
    Publication date: November 12, 2020
    Applicant: Enlitic, Inc.
    Inventors: Li Yao, Kevin Lyman, Ashwin Jadhav, Eric C. Poblenz, 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: 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: 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: 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: 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: 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: 20200160946
    Abstract: A longitudinal data quality assurance system is operable to receive a set of medical scans corresponding to a same first patient. A first chronologically ordered list of the set of medical scans is generated based on a corresponding first set of dates, where each of the corresponding first set of dates are extracted from a headers of the set of medical scans. Quality assurance data is generated for the first chronologically ordered list by performing at least one quality assurance function on at least one of the set of medical scans. A second chronologically ordered list that includes a first subset of the first set of medical scans is generated to rectify at least one continuity error of the first chronologically ordered list, indicated in the quality assurance data. The second chronologically ordered list is transmitted to a client device for display via a display device.
    Type: Application
    Filed: March 18, 2019
    Publication date: May 21, 2020
    Applicant: Enlitic, Inc.
    Inventors: Eric C. Poblenz, Li Yao, Keith Lui, Kevin Lyman