Patents by Inventor Christopher Kanan

Christopher Kanan 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: 11481898
    Abstract: Systems and methods are disclosed for receiving digital images of a pathology specimen from a patient, the pathology specimen comprising tumor tissue, the one or more digital images being associated with data about a plurality of biomarkers in the tumor tissue and data about a surrounding invasive margin around the tumor tissue; identifying the tumor tissue and the surrounding invasive margin region to be analyzed for each of the one or more digital images; generating, using a machine learning model on the one or more digital images, at least one inference of a presence of the plurality of biomarkers in the tumor tissue and the surrounding invasive margin region; determining a spatial relationship of each of the plurality of biomarkers identified in the tumor tissue and the surrounding invasive margin region to themselves and to other cell types; and determining a prediction for a treatment outcome and/or at least one treatment recommendation for the patient.
    Type: Grant
    Filed: November 4, 2021
    Date of Patent: October 25, 2022
    Assignee: Paige.AI, Inc.
    Inventors: Belma Dogdas, Christopher Kanan, Thomas Fuchs, Leo Grady
  • Patent number: 11481899
    Abstract: A computer-implemented method may include receiving a collection of unstained digital histopathology slide images at a storage device and running a trained machine learning model on one or more slide images of the collection to infer a presence or an absence of a salient feature. The trained machine learning model may have been trained by processing a second collection of unstained or stained digital histopathology slide images and at least one synoptic annotation for one or more unstained or stained digital histopathology slide images of the second collection. The computer-implemented method may further include determining at least one map from output of the trained machine learning model and providing an output from the trained machine learning model to the storage device.
    Type: Grant
    Filed: December 10, 2021
    Date of Patent: October 25, 2022
    Assignee: PAIGE.AI, Inc.
    Inventors: Patricia Raciti, Christopher Kanan, Alican Bozkurt, Belma Dogdas
  • Publication number: 20220335607
    Abstract: Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to identify a region of interest of the target specimen and determine an expression level of, category of, and/or presence of a biomarker in the region of interest, the biomarker comprising at least one from among an epithelial growth factor receptor (EGFR) biomarker and/or a DNA mismatch repair (MMR) deficiency biomarker, the machine learning system having been generated by processing a plurality of training images to predict whether a region of interest is present in the target electronic image, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the determined expression level of, category of, and/or presence of the biomarker in the region of interest.
    Type: Application
    Filed: July 5, 2022
    Publication date: October 20, 2022
    Inventors: Supriya KAPUR, Ran GODRICH, Christopher KANAN, Thomas FUCHS, Leo GRADY
  • Patent number: 11475990
    Abstract: Systems and methods are disclosed for receiving one or more digital images associated with a tissue specimen, a related case, a patient, and/or a plurality of clinical information, determining one or more of a prediction, a recommendation, and/or a plurality of data for the one or more digital images using a machine learning system, the machine learning system having been trained using a plurality of training images, to predict a biomarker and a plurality of genomic panel elements, and determining, based on the prediction, the recommendation, and/or the plurality of data, whether to log an output and at least one visualization region as part of a case history within a clinical reporting system.
    Type: Grant
    Filed: January 27, 2021
    Date of Patent: October 18, 2022
    Assignee: Paige.AI, Inc.
    Inventors: Jillian Sue, Jason Locke, Peter Schueffler, Christopher Kanan, Thomas Fuchs, Leo Grady
  • Patent number: 11475566
    Abstract: Systems and methods are disclosed for processing digital images to predict at least one continuous value comprising receiving one or more digital medical images, determining whether the one or more digital medical images includes at least one salient region, upon determining that the one or more digital medical images includes the at least one salient region, predicting, by a trained machine learning system, at least one continuous value corresponding to the at least one salient region, and outputting the at least one continuous value to an electronic storage device and/or display.
    Type: Grant
    Filed: August 24, 2021
    Date of Patent: October 18, 2022
    Assignee: PAIGE.AI, Inc.
    Inventors: Christopher Kanan, Belma Dogdas, Patricia Raciti, Matthew Lee, Alican Bozkurt, Leo Grady, Thomas Fuchs, Jorge S. Reis-Filho
  • Patent number: 11475989
    Abstract: Systems and methods are disclosed for providing automated routing of medical data, comprising determining at least one rule corresponding to at least one condition and at least one receiver, receiving medical data and associated medical metadata, determining whether the medical data, the associated medical metadata, and/or associated artificial intelligence processing satisfies the at least one condition of the at least one rule, and upon determining that the at least one condition of the at least one rule is satisfied, providing, from an originating institution, the medical data to the at least one receiver.
    Type: Grant
    Filed: August 11, 2021
    Date of Patent: October 18, 2022
    Assignee: PAIGE.AI, Inc.
    Inventors: Jeremy Daniel Kunz, Christopher Kanan, Patricia Raciti, Matthew G. Hanna
  • Patent number: 11455724
    Abstract: Systems and methods are disclosed for adjusting attributes of whole slide images, including stains therein. A portion of a whole slide image comprised of a plurality of pixels in a first color space and including one or more stains may be received as input. Based on an identified stain type of the stain(s), a machine-learned transformation associated with the stain type may be retrieved and applied to convert an identified subset of the pixels from the first to a second color space specific to the identified stain type. One or more attributes of the stain(s) may be adjusted in the second color space to generate a stain-adjusted subset of pixels, which are then converted back to the first color space using an inverse of the machine-learned transformation. A stain-adjusted portion of the whole slide image including at least the stain-adjusted subset of pixels may be provided as output.
    Type: Grant
    Filed: December 7, 2021
    Date of Patent: September 27, 2022
    Assignee: PAIGE.AI, Inc.
    Inventors: Navid Alemi, Christopher Kanan, Leo Grady
  • Patent number: 11455753
    Abstract: Systems and methods are disclosed for adjusting attributes of whole slide images, including stains therein. A portion of a whole slide image comprised of a plurality of pixels in a first color space and including one or more stains may be received as input. Based on an identified stain type of the stain(s), a machine-learned transformation associated with the stain type may be retrieved and applied to convert an identified subset of the pixels from the first to a second color space specific to the identified stain type. One or more attributes of the stain(s) may be adjusted in the second color space to generate a stain-adjusted subset of pixels, which are then converted back to the first color space using an inverse of the machine-learned transformation. A stain-adjusted portion of the whole slide image including at least the stain-adjusted subset of pixels may be provided as output.
    Type: Grant
    Filed: December 7, 2021
    Date of Patent: September 27, 2022
    Assignee: PAIGE.AI, Inc.
    Inventors: Navid Alemi, Christopher Kanan, Leo Grady
  • Publication number: 20220293251
    Abstract: Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared.
    Type: Application
    Filed: May 26, 2022
    Publication date: September 15, 2022
    Inventors: Rodrigo CEBALLOS LENTINI, Christopher KANAN, Patricia RACITI, Leo GRADY, Thomas FUCHS
  • Publication number: 20220293242
    Abstract: A computer-implemented method may include receiving a collection of unstained digital histopathology slide images at a storage device and running a trained machine learning model on one or more slide images of the collection to infer a presence or an absence of a salient feature. The trained machine learning model may have been trained by processing a second collection of unstained or stained digital histopathology slide images and at least one synoptic annotation for one or more unstained or stained digital histopathology slide images of the second collection. The computer-implemented method may further include determining at least one map from output of the trained machine learning model and providing an output from the trained machine learning model to the storage device.
    Type: Application
    Filed: December 3, 2021
    Publication date: September 15, 2022
    Inventors: Patricia RACITI, Christopher KANAN, Alican BOZKURT, Belma DOGDAS
  • Publication number: 20220293248
    Abstract: Systems and methods are disclosed for verifying slide and block quality for testing. The method may comprise receiving a collection of one or more digital images at a digital storage device. The collection may be associated with a tissue block and corresponding to an instance. The method may comprise applying a machine learning model to the collection to identify a presence or an absence of an attribute, determining an amount or a percentage of tissue with the attribute from a digital image in the collection that indicates the presence of the attribute, and outputting a quality score corresponding to the determined amount or percentage.
    Type: Application
    Filed: December 1, 2021
    Publication date: September 15, 2022
    Inventors: Patricia RACITI, Christopher KANAN, Alican BOZKURT, Belma DOGDAS
  • Publication number: 20220292670
    Abstract: A computer-implemented method may include receiving a collection of unstained digital histopathology slide images at a storage device and running a trained machine learning model on one or more slide images of the collection to infer a presence or an absence of a salient feature. The trained machine learning model may have been trained by processing a second collection of unstained or stained digital histopathology slide images and at least one synoptic annotation for one or more unstained or stained digital histopathology slide images of the second collection. The computer-implemented method may further include determining at least one map from output of the trained machine learning model and providing an output from the trained machine learning model to the storage device.
    Type: Application
    Filed: December 10, 2021
    Publication date: September 15, 2022
    Inventors: Patricia RACITI, Christopher KANAN, Alican BOZKURT, Belma DOGDAS
  • Publication number: 20220293249
    Abstract: Systems and methods are disclosed for verifying slide and block quality for testing. The method may comprise receiving a collection of one or more digital images at a digital storage device. The collection may be associated with a tissue block and corresponding to an instance. The method may comprise applying a machine learning model to the collection to identify a presence or an absence of an attribute, determining an amount or a percentage of tissue with the attribute from a digital image in the collection that indicates the presence of the attribute, and outputting a quality score corresponding to the determined amount or percentage.
    Type: Application
    Filed: December 30, 2021
    Publication date: September 15, 2022
    Inventors: Patricia RACITI, Christopher KANAN, Alican BOZKURT, Belma DOGDAS
  • Patent number: 11436727
    Abstract: Systems and methods are disclosed for grouping cells in a slide image that share a similar target, comprising receiving a digital pathology image corresponding to a tissue specimen, applying a trained machine learning system to the digital pathology image, the trained machine learning system being trained to predict at least one target difference across the tissue specimen, and determining, using the trained machine learning system, one or more predicted clusters, each of the predicted clusters corresponding to a subportion of the tissue specimen associated with a target.
    Type: Grant
    Filed: August 18, 2021
    Date of Patent: September 6, 2022
    Assignee: PAIGE.AI, Inc.
    Inventors: Rodrigo Ceballos Lentini, Christopher Kanan, Belma Dogdas
  • Patent number: 11430116
    Abstract: Systems and methods are disclosed for identifying formerly conjoined pieces of tissue in a specimen, comprising receiving one or more digital images associated with a pathology specimen, identifying a plurality of pieces of tissue by applying an instance segmentation system to the one or more digital images, the instance segmentation system having been generated by processing a plurality of training images, determining, using the instance segmentation system, a prediction of whether any of the plurality of pieces of tissue were formerly conjoined, and outputting at least one instance segmentation to a digital storage device and/or display, the instance segmentation comprising an indication of whether any of the plurality of pieces of tissue were formerly conjoined.
    Type: Grant
    Filed: September 9, 2021
    Date of Patent: August 30, 2022
    Assignee: PAIGE.AI, Inc.
    Inventors: Antoine Sainson, Brandon Rothrock, Razik Yousfi, Patricia Raciti, Matthew Hanna, Christopher Kanan
  • Patent number: 11430117
    Abstract: Systems and methods are disclosed for identifying formerly conjoined pieces of tissue in a specimen, comprising receiving one or more digital images associated with a pathology specimen, identifying a plurality of pieces of tissue by applying an instance segmentation system to the one or more digital images, the instance segmentation system having been generated by processing a plurality of training images, determining, using the instance segmentation system, a prediction of whether any of the plurality of pieces of tissue were formerly conjoined, and outputting at least one instance segmentation to a digital storage device and/or display, the instance segmentation comprising an indication of whether any of the plurality of pieces of tissue were formerly conjoined.
    Type: Grant
    Filed: October 4, 2021
    Date of Patent: August 30, 2022
    Assignee: PAIGE.AI, Inc.
    Inventors: Antoine Sainson, Brandon Rothrock, Razik Yousfi, Patricia Raciti, Matthew Hanna, Christopher Kanan
  • Patent number: 11423547
    Abstract: Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.
    Type: Grant
    Filed: September 21, 2021
    Date of Patent: August 23, 2022
    Assignee: Paige.AI, Inc.
    Inventors: Brandon Rothrock, Christopher Kanan, Julian Viret, Thomas Fuchs, Leo Grady
  • Publication number: 20220230734
    Abstract: Systems and methods are disclosed for generating a specialized machine learning model by receiving a generalized machine learning model generated by processing a plurality of first training images to predict at least one cancer characteristic, receiving a plurality of second training images, the first training images and the second training images include images of tissue specimens and/or images algorithmically generated to replicate tissue specimens, receiving a plurality of target specialized attributes related to a respective second training image of the plurality of second training images, generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of second training images and the target specialized attributes, receiving a target image corresponding to a target specimen, applying the specialized machine learning model to the target image to determine at least one characteristic of the target image, and outputting the characteristic of the tar
    Type: Application
    Filed: March 31, 2022
    Publication date: July 21, 2022
    Inventors: Belma DOGDAS, Christopher KANAN, Thomas FUCHS, Leo GRADY
  • Patent number: 11393574
    Abstract: Systems and methods are disclosed for generating synthetic medical images, including images presenting rare conditions or morphologies for which sufficient data may be unavailable. In one aspect, style transfer methods may be used. For example, a target medical image, a segmentation mask identifying style(s) to be transferred to area(s) of the target, and source medical image(s) including the style(s) may be received. Using the mask, the target may be divided into tile(s) corresponding to the area(s) and input to a trained machine learning system. For each tile, gradients associated with a content and style of the tile may be output by the system. Pixel(s) of at least one tile of the target may be altered based on the gradients to maintain content of the target while transferring the style(s) of the source(s) to the target. The synthetic medical image may be generated from the target based on the altering.
    Type: Grant
    Filed: December 20, 2021
    Date of Patent: July 19, 2022
    Assignee: PAIGE.AI, Inc.
    Inventors: Rodrigo Ceballos Lentini, Christopher Kanan
  • Patent number: 11393575
    Abstract: Systems and methods are disclosed for generating synthetic medical images, including images presenting rare conditions or morphologies for which sufficient data may be unavailable. In one aspect, style transfer methods may be used. For example, a target medical image, a segmentation mask identifying style(s) to be transferred to area(s) of the target, and source medical image(s) including the style(s) may be received. Using the mask, the target may be divided into tile(s) corresponding to the area(s) and input to a trained machine learning system. For each tile, gradients associated with a content and style of the tile may be output by the system. Pixel(s) of at least one tile of the target may be altered based on the gradients to maintain content of the target while transferring the style(s) of the source(s) to the target. The synthetic medical image may be generated from the target based on the altering.
    Type: Grant
    Filed: December 20, 2021
    Date of Patent: July 19, 2022
    Assignee: PAIGE.AI, Inc.
    Inventors: Rodrigo Ceballos Lentini, Christopher Kanan