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: 11676704
    Abstract: Systems and methods are disclosed for determining at least one geographic region of a plurality of geographic regions, at least one data variable, and/or at least one health variable, estimating a current prevalence of a data variable in a geographic region of the plurality of geographic regions, determining a trend in a relationship between the data variable and the geographic region at a current time, determining a second trend in the relationship between the data variable and the geographic region at at least one prior point in time, determining if the trend in the relationship is irregular within a predetermined threshold with respect to the second trend from the at least one prior point in time, and, upon determining that the trend in the relationship is irregular within a predetermined threshold, generating an alert.
    Type: Grant
    Filed: November 30, 2020
    Date of Patent: June 13, 2023
    Assignee: Paige.AI, Inc.
    Inventors: Christopher Kanan, Rodrigo Ceballos Lentini, Jillian Sue, Thomas Fuchs, Leo Grady
  • Patent number: 11676274
    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: Grant
    Filed: March 14, 2022
    Date of Patent: June 13, 2023
    Assignee: Paige.AI, Inc.
    Inventors: Rodrigo Ceballos Lentini, Christopher Kanan, Patricia Raciti, Leo Grady, Thomas Fuchs
  • Publication number: 20230143593
    Abstract: The present disclosure is directed to systems and methods of maintaining databases of biomedical images. A server may aggregate digital pathology records from data sources onto a database. Each record may be generated by a data source using a format, and may identify a biomedical image of a sample and data identifying a subject from which the sample is obtained. The server may receive, from a client device, a query identifying a criterion. The server may access the database to identify a subset of records using the criterion. For each record of the subset, the server may identify a data source that generated the record. The server may select a de-identification policy to apply based on the data source. The server may modify the data in the record according to the de-identification policy and the format. The server may provide, to the client device, the de-identified record.
    Type: Application
    Filed: March 15, 2021
    Publication date: May 11, 2023
    Inventors: Thomas Fuchs, Luke Geneslaw, Dig Vijay Kumar Yarlagadda
  • Publication number: 20230147471
    Abstract: Systems and methods are disclosed for identifying a diagnostic feature of a digitized pathology image, including receiving one or more digitized images of a pathology specimen, and medical metadata comprising at least one of image metadata, specimen metadata, clinical information, and/or patient information, applying a machine learning model to predict a plurality of relevant diagnostic features based on medical metadata, the machine learning model having been developed using an archive of processed images and prospective patient data, and determining at least one relevant diagnostic feature of the relevant diagnostic features for output to a display.
    Type: Application
    Filed: January 5, 2023
    Publication date: May 11, 2023
    Inventors: Jillian SUE, Thomas FUCHS, Christopher KANAN
  • Publication number: 20230144137
    Abstract: Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.
    Type: Application
    Filed: January 4, 2023
    Publication date: May 11, 2023
    Inventors: Supriya KAPUR, Christopher KANAN, Thomas FUCHS, Leo GRADY
  • Patent number: 11640719
    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: July 12, 2022
    Date of Patent: May 2, 2023
    Assignee: Paige.AI, Inc.
    Inventors: Brandon Rothrock, Christopher Kanan, Julian Viret, Thomas Fuchs, Leo Grady
  • Patent number: 11636696
    Abstract: The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not.
    Type: Grant
    Filed: March 25, 2021
    Date of Patent: April 25, 2023
    Assignee: Memorial Sloan Kettering Cancer Center
    Inventors: Andrew Schaumberg, Thomas Fuchs
  • Publication number: 20230111077
    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: Application
    Filed: October 14, 2022
    Publication date: April 13, 2023
    Inventors: Christopher KANAN, Belma DOGDAS, Patricia RACITI, Matthew LEE, Alican BOZKURT, Leo GRADY, Thomas FUCHS, Jorge S. REIS-FILHO
  • Publication number: 20230100881
    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: Application
    Filed: October 3, 2022
    Publication date: March 30, 2023
    Inventors: Thomas FUCHS, David Joon HO
  • Patent number: 11615534
    Abstract: Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, determining a quality control (QC) machine learning model to predict a quality designation based on one or more artifacts, providing the digital image as an input to the QC machine learning model, receiving the quality designation for the digital image as an output from the machine learning model, and outputting the quality designation of the digital image. A quality assurance (QA) machine learning model may predict a disease designation based on one or more biomarkers. The digital image may be provided to the QA model which may output a disease designation. An external designation may be compared to the disease designation and a comparison result may be output.
    Type: Grant
    Filed: December 2, 2021
    Date of Patent: March 28, 2023
    Assignee: Paige.AI, Inc.
    Inventors: Jillian Sue, Razik Yousfi, Peter Schueffler, Thomas Fuchs, Leo Grady
  • Publication number: 20230082710
    Abstract: The present disclosure is directed to systems and methods for classifying biomedical images. A feature classifier may generate a plurality of tiles from a biomedical image. Each tile may correspond to a portion of the biomedical image. The feature classifier may select a subset of tiles from the plurality of tiles by applying an inference model. The subset of tiles may have highest scores. Each score may indicate a likelihood that the corresponding tile includes a feature indicative of the presence of the condition. The feature classifier may determine a classification result for the biomedical image by applying an aggregation model. The classification result may indicate whether the biomedical includes the presence or lack of the condition.
    Type: Application
    Filed: November 10, 2022
    Publication date: March 16, 2023
    Applicant: Memorial Sloan Kettering Cancer Center
    Inventors: Thomas Fuchs, Gabriele Campanella
  • Publication number: 20230070874
    Abstract: Presented herein are systems and methods for classifying features from biomedical images. A computing system may identify a first portion corresponding to an ROI in a first biomedical image derived from a sample. The ROI of the first biomedical image may correspond to a feature of the sample. The computing system may generate a first embedding vector using the first portion of the first biomedical image. The computing system may apply the first embedding vector to a clustering model. The clustering model may have a feature space to define a plurality of conditions. The clustering model may be trained using a second embedding vectors generated from a corresponding second portions with at least one of a plurality of image transformation. The computing system may determine a condition for the feature based on applying the first embedding vector to the clustering model.
    Type: Application
    Filed: September 1, 2022
    Publication date: March 9, 2023
    Applicant: MEMORIAL SLOAN KETTERING CANCER CENTER
    Inventors: Chao FENG, Chad VANDERBILT, Thomas FUCHS
  • Patent number: 11593684
    Abstract: Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model, which may also be known as a machine learning system, to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.
    Type: Grant
    Filed: March 28, 2022
    Date of Patent: February 28, 2023
    Assignee: Paige.AI, Inc.
    Inventors: Supriya Kapur, Christopher Kanan, Thomas Fuchs, Leo Grady
  • Patent number: 11574140
    Abstract: Systems and methods are disclosed for identifying a diagnostic feature of a digitized pathology image, including receiving one or more digitized images of a pathology specimen, and medical metadata comprising at least one of image metadata, specimen metadata, clinical information, and/or patient information, applying a machine learning model to predict a plurality of relevant diagnostic features based on medical metadata, the machine learning model having been developed using an archive of processed images and prospective patient data, and determining at least one relevant diagnostic feature of the relevant diagnostic features for output to a display.
    Type: Grant
    Filed: May 6, 2021
    Date of Patent: February 7, 2023
    Assignee: Paige.AI, Inc.
    Inventors: Jillian Sue, Thomas Fuchs, Christopher Kanan
  • Publication number: 20230030216
    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: Application
    Filed: October 5, 2022
    Publication date: February 2, 2023
    Inventors: Belma DOGDAS, Christopher KANAN, Thomas FUCHS, Leo GRADY, Kenan TURNACIOGLU
  • Patent number: 11565585
    Abstract: A motor vehicle tank that includes a tank container formed by a tank wall, and a holding element to fasten a component to the tank wall at an interior of the tank container. The holding element has at least one attachment point for fastening the holding element to the tank wall, at least one fastening element for fastening the component to the holding element, and a plurality of spring elements arranged between the at least one attachment point and the fastening element to facilitate fastening of the component to the tank wall in a spring-elastically decoupled manner.
    Type: Grant
    Filed: June 22, 2021
    Date of Patent: January 31, 2023
    Assignee: MAGNA Energy Storage Systems GesmbH
    Inventors: Thomas Fuchs, Laura Heidenbauer
  • Publication number: 20230025189
    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: Application
    Filed: September 21, 2022
    Publication date: January 26, 2023
    Inventors: Belma DOGDAS, Christopher KANAN, Thomas FUCHS, Leo GRADY
  • Publication number: 20230021031
    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: Application
    Filed: September 16, 2022
    Publication date: January 19, 2023
    Applicant: MEMORIAL SLOAN KETTERING CANCER CENTER
    Inventors: Dig Vijay Kumar YARLAGADDA, Matthew HANNA, Peter SCHUEFFLER, Thomas FUCHS
  • Publication number: 20230005597
    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: Application
    Filed: September 12, 2022
    Publication date: January 5, 2023
    Inventors: Jillian SUE, Jason LOCKE, Peter SCHUEFFLER, Christopher KANAN, Thomas FUCHS, Leo GRADY
  • Patent number: 11538155
    Abstract: The present disclosure is directed to systems and methods for classifying biomedical images. A feature classifier may generate a plurality of tiles from a biomedical image. Each tile may correspond to a portion of the biomedical image. The feature classifier may select a subset of tiles from the plurality of tiles by applying an inference model. The subset of tiles may have highest scores. Each score may indicate a likelihood that the corresponding tile includes a feature indicative of the presence of the condition. The feature classifier may determine a classification result for the biomedical image by applying an aggregation model. The classification result may indicate whether the biomedical includes the presence or lack of the condition.
    Type: Grant
    Filed: October 19, 2020
    Date of Patent: December 27, 2022
    Assignee: MEMORIAL SLOAN KETTERING CANCER CENTER
    Inventors: Thomas Fuchs, Gabriele Campanella