Patents by Inventor Thomas Fuchs

Thomas Fuchs 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: 11508066
    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 11, 2021
    Date of Patent: November 22, 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: 11501434
    Abstract: Described herein are Deep Multi-Magnification Networks (DMMNs). The multi-class tissue segmentation architecture processes a set of patches from multiple magnifications to make more accurate predictions. For the supervised training, partial annotations may be used to reduce the burden of annotators. The segmentation architecture with multi-encoder, multi-decoder, and multi-concatenation outperforms other segmentation architectures on breast datasets, and can be used to facilitate pathologists' assessments of breast cancer in margin specimens.
    Type: Grant
    Filed: October 2, 2020
    Date of Patent: November 15, 2022
    Assignee: MEMORIAL SLOAN KETTERING CANCER CENTER
    Inventors: Thomas Fuchs, David Joon Ho
  • Patent number: 11494907
    Abstract: Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of tissue specimen, determining a detection machine learning model, the detection machine learning model being generated by processing a plurality of training images to output a cancer qualification and further a cancer quantification if the cancer qualification is an confirmed cancer qualification, providing the digital image as an input to the detection machine learning model, receiving one of a pathological complete response (pCR) cancer qualification or a confirmed cancer quantification as an output from the detection machine learning model, and outputting the pCR cancer qualification or the confirmed cancer quantification.
    Type: Grant
    Filed: December 16, 2020
    Date of Patent: November 8, 2022
    Assignee: PAIGE.AI, INC.
    Inventors: Belma Dogdas, Christopher Kanan, Thomas Fuchs, Leo Grady, Kenan Turnacioglu
  • Publication number: 20220343508
    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: Application
    Filed: July 12, 2022
    Publication date: October 27, 2022
    Inventors: Brandon ROTHROCK, Christopher KANAN, Julian VIRET, Thomas FUCHS, Leo GRADY
  • 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
  • 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
  • Publication number: 20220328190
    Abstract: An image processing method including identifying, using a machine learning system, an area of interest of a target image by analyzing features extracted from image regions in the target image, the machine learning system being generated by processing a plurality of training images each comprising an image of human tissue and a diagnostic label characterizing at least one of a slide morphology, a diagnostic value, and a pathologist review outcome; determining, using the machine learning system, a probability of a target feature being present in the area of interest of the target image based on an average probability; determining, using the machine learning system, a prioritization value, of a plurality of prioritization values, of the target image based on the probability of the target feature being present in the target image.
    Type: Application
    Filed: June 28, 2022
    Publication date: October 13, 2022
    Inventors: Ran GODRICH, Jillian SUE, Leo GRADY, Thomas FUCHS
  • Patent number: 11456077
    Abstract: An image processing method including identifying, using a machine learning system, an area of interest of a target image by analyzing microscopic features extracted from multiple image regions in the target image, the machine learning system being generated by processing a plurality of training images each comprising an image of human tissue and a diagnostic label characterizing at least one of a slide morphology, a diagnostic value, a pathologist review outcome, and an analytic difficulty; determining, using the machine learning system, a probability of a target feature being present in the area of interest of the target image based on an average probability; and determining, using the machine learning system, a prioritization value, of a plurality of prioritization values, of the target image based on the probability of the target feature being present in the target image.
    Type: Grant
    Filed: November 18, 2021
    Date of Patent: September 27, 2022
    Assignee: Paige.AI, Inc.
    Inventors: Ran Godrich, Jillian Sue, Leo Grady, Thomas Fuchs
  • Publication number: 20220301691
    Abstract: The present disclosure relates generally to image viewers, in particular biomedical images viewers that can concurrently render biomedical images at various magnifications or resolutions. The computing system can identify tiles from a first portion of a biomedical image. Each tile can correspond to a magnification level and coordinates in the biomedical image. The computing system can provide the tiles for concurrent display in respective graphical user interface (GUI) elements. The computing system can detect an interaction with a GUI element. The computing system can identify a change in coordinates for a tile displayed in the GUI element based on the interaction. The computing system can determine a second change in the other concurrently displayed tiles based on the change in coordinates. The computing system can update the concurrent display of the tiles based on the changes.
    Type: Application
    Filed: August 24, 2020
    Publication date: September 22, 2022
    Inventors: Peter SCHUEFFLER, Thomas FUCHS
  • Patent number: 11449994
    Abstract: Described herein are systems and methods of determining primary sites from biomedical images. A computing system may identify a first biomedical image of a first sample from one of a primary site or a secondary site associated with a condition in a first subject. The computing system may apply the first biomedical image to a site prediction model comprising a plurality of weights to determine the primary site for the condition. The computing system may store an association between the first biomedical image and the primary site determined using the site prediction model.
    Type: Grant
    Filed: June 1, 2021
    Date of Patent: September 20, 2022
    Assignee: MEMORIAL SLOAN KETTERING CANCER CENTER
    Inventors: Dig Vijay Kumar Yarlagadda, Matthew Hanna, Peter Schueffler, Thomas Fuchs
  • 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: 20220292681
    Abstract: The present application relates generally to image tiling, including but not limited to systems and methods of fast whole slide tissue tiling. A computing system may identify a first image of a first dimension from which to select one or more tiles. The computing system may perform a reduction operation on the first image to generate a second image of a second dimension. The computing system may apply a thresholding operation on the second image to identify a first set of pixels corresponding to the presence of the feature and a second set of pixels corresponding to the absence of the feature based on an intensity of each pixel in the second image. The computing system may select, from a plurality of tiles corresponding to the first image, a subset of tiles corresponding to the first set of pixels identified from the second image.
    Type: Application
    Filed: August 24, 2020
    Publication date: September 15, 2022
    Inventors: Gabriele CAMPANELLA, Thomas FUCHS
  • Patent number: 11444254
    Abstract: The present invention relates to metal-carbene complexes comprising a central atom selected from iridium and platinum, and diazabenzimidazolocarbene ligands, to organic light diodes which comprise such complexes, to light-emitting layers comprising at least one such metal-carbene complex, to a device selected from the group comprising illuminating elements, stationary visual display units and mobile visual display units comprising such an OLED and to the use of such a metal-carbene complex in OLEDs, for example as emitter, matrix material, charge transport material and/or charge or exciton blocker.
    Type: Grant
    Filed: December 23, 2020
    Date of Patent: September 13, 2022
    Assignee: UDC IRELAND LIMITED
    Inventors: Oliver Molt, Nicolle Langer, Evelyn Fuchs, Korinna Dormann, Christian Schildknecht, Soichi Watanabe, Gerhard Wagenblast, Christian Lennartz, Thomas Schaefer, Heinz Wolleb, Teresa Marina Figueira Duarte, Stefan Metz, Peter Murer
  • Publication number: 20220286010
    Abstract: A drive unit for motor vehicle applications, in particular to a locking system drive unit. The latter has an enclosure consisting substantially of a housing and cover. In addition, an electric motor is realized inside the enclosure. The electric motor is receiving with its two motor bearings in associated cutouts in the housing and is fixed by joining together the housing and the cover. According to the invention, the housing and/or the cover have/has fixing anchors in a central region that are arranged on both sides of the cutouts.
    Type: Application
    Filed: August 17, 2020
    Publication date: September 8, 2022
    Inventors: Stephan MEUTERS, Thomas HÜLSMANN, Carsten FUCHS
  • 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
  • Patent number: 11418124
    Abstract: The present invention relates to a circuit for switching an AC voltage. It contains an input terminal able to be connected to an AC voltage source, an output terminal able to be connected to a load impedance, and a first series circuit. This series circuit comprises a diode and a circuit for storing electrical charges. The series circuit has a first end connection that is connected to the input terminal and a second end connection that is connected to the output terminal. The circuit for switching an AC voltage furthermore contains a DC voltage source, which is connected to an electrical connection between the diode and the input terminal or to an electrical connection between the diode and the output terminal and is designed to impress a DC current in the diode. The circuit for switching an AC voltage finally contains a first switch that is connected to an electrical connection between the diode and the circuit for storing electrical charges at one terminal.
    Type: Grant
    Filed: May 10, 2019
    Date of Patent: August 16, 2022
    Assignee: ROSENBERGER HOCHFREQUENZTECHNIK GMBH
    Inventors: Martin Fuchs, Christoph Huber, Johannes Winkler, Sven Gröger, Marcel Van Delden, Gordon Notzon, Thomas Musch
  • Publication number: 20220236447
    Abstract: A method includes determining a present-day thickness of a lithosphere. The method also includes determining whether the determined present-day thickness of the lithosphere substantially matches an interpreted present-day thickness of the lithosphere. The method also includes generating or updating a temperature history model in response to determining that the determined present-day thickness of the lithosphere substantially matches the interpreted present-day thickness of the lithosphere.
    Type: Application
    Filed: June 4, 2020
    Publication date: July 28, 2022
    Inventors: Daniel B. PALMOWSKI, Thomas FUCHS, Adrian KLEINE
  • 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