Patents Assigned to PathAI, Inc.
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Patent number: 11915823Abstract: In some aspects, the described systems and methods provide for validating performance of a model trained on a plurality of annotated pathology images. A pathology image is accessed. Frames are generated using the pathology image. Each frame in the set includes a distinct portion of the pathology image. Reference annotations are received from one or more users. The reference annotations describe at least one of a plurality of tissue or cellular characteristic categories for one or more frames in the set. Each frame in the set is processed using the trained model to generate model predictions. The model predictions describe at least one of the tissue or cellular characteristic categories for the processed frame. Performance of the trained model is validated based on determining a degree of association between the reference annotations and the model predictions for each frame and/or across all frames in the set of frames.Type: GrantFiled: November 10, 2022Date of Patent: February 27, 2024Assignee: PathAI, Inc.Inventors: Harsha Vardhan Pokkalla, Hunter L. Elliott, Dayong Wang, Benjamin P. Glass, Ilan N. Wapinski, Jennifer K. Kerner, Andrew H. Beck, Aditya Khosla, Sai Chowdary Gullapally, Ramprakash Srinivasan
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Patent number: 11908139Abstract: In some aspects, the described systems and methods provide for a method for training a statistical model to predict tissue characteristics for a pathology image. The method includes accessing annotated pathology images. Each of the images includes an annotation describing a tissue characteristic category for a portion of the image. A set of training patches and a corresponding set of annotations are defined using an annotated pathology image. Each of the training patches in the set includes values obtained from a respective subset of pixels in the annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the respective subset of pixels. The statistical model is trained based on the set of training patches and the corresponding set of patch annotations. The trained statistical model is stored on at least one storage device.Type: GrantFiled: July 13, 2023Date of Patent: February 20, 2024Assignee: PathAI, Inc.Inventors: Andrew H. Beck, Aditya Khosla
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Publication number: 20230326022Abstract: A method includes receiving an input histology image, processing, using a cell classification model, the input histology image to generate one or more lymphocyte density maps within the input histology image, and performing morphological image processing on the one or more lymphocyte density maps to identify one or more TLS regions within the input histology image. Each TLS region is represented by a respective cluster of lymphocyte cells. For each corresponding TLS region of the one or more TLS regions identified in the input histology image, the method also includes extracting, from the respective cluster of lymphocyte cells, a respective set of TLS features, and processing, using a TLS classification model, the respective set of TLS features to classify the corresponding TLS region as one of a first TLS maturation state, a second TLS maturation state, or a third TLS maturation state.Type: ApplicationFiled: April 7, 2023Publication date: October 12, 2023Applicants: Bristol-Myers Squibb Company, PathAI, Inc.Inventors: Vanessa Matos-Cruz, George Lee, Varsha Chinnaobireddy, Maryam Pouryahya, Darren Thomas Fahy, Christian Winskell Kirkup, Kathleen Sucipto, Sai Chowdary Gullapally, Archit Khosla, Nishant Agrawal, Benjamin Patrick Glass, Sergine Brutus, Limin Yu, Murray Berle Resnick, Rachel L. Sargent, Vipul Atulkumar Baxi, Scott Ely, Benjamin J. Chen
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Patent number: 11756198Abstract: In some aspects, the described systems and methods provide for a method for training a statistical model to predict tissue characteristics for a pathology image. The method includes accessing annotated pathology images. Each of the images includes an annotation describing a tissue characteristic category for a portion of the image. A set of training patches and a corresponding set of annotations are defined using an annotated pathology image. Each of the training patches in the set includes values obtained from a respective subset of pixels in the annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the respective subset of pixels. The statistical model is trained based on the set of training patches and the corresponding set of patch annotations. The trained statistical model is stored on at least one storage device.Type: GrantFiled: December 6, 2021Date of Patent: September 12, 2023Assignee: PathAI, Inc.Inventors: Andrew H. Beck, Aditya Khosla
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Patent number: 11657505Abstract: In some aspects, the described systems and methods provide for a method for training a model to predict survival time for a patient. The method includes accessing annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.Type: GrantFiled: May 24, 2021Date of Patent: May 23, 2023Assignee: PathAI, Inc.Inventors: Andrew H. Beck, Aditya Khosla
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Patent number: 11527319Abstract: In some aspects, the described systems and methods provide for validating performance of a model trained on a plurality of annotated pathology images. A pathology image is accessed. Frames are generated using the pathology image. Each frame in the set includes a distinct portion of the pathology image. Reference annotations are received from one or more users. The reference annotations describe at least one of a plurality of tissue or cellular characteristic categories for one or more frames in the set. Each frame in the set is processed using the trained model to generate model predictions. The model predictions describe at least one of the tissue or cellular characteristic categories for the processed frame. Performance of the trained model is validated based on determining a degree of association between the reference annotations and the model predictions for each frame and/or across all frames in the set of frames.Type: GrantFiled: September 11, 2020Date of Patent: December 13, 2022Assignee: PathAI, Inc.Inventors: Harsha Vardhan Pokkalla, Hunter L. Elliott, Dayong Wang, Benjamin P. Glass, Ilan N. Wapinski, Jennifer K. Kerner, Andrew H. Beck, Aditya Khosla, Sai Chowdary Gullapally, Ramprakash Srinivasan
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Publication number: 20220245802Abstract: Techniques for classifying biomedical image data using a graph neural network are disclosed. In one particular embodiment, the techniques may be realized as a method for classifying biomedical image data comprising generating an annotated representation of biomedical image data; identifying a plurality of pixel clusters based on the biomedical image data; constructing a graph based on the plurality of pixel clusters; determining at least one biomedical feature for at least one node of the graph based on the annotated representation of the biomedical image data; and processing a graph neural network to classify the biomedical image data based on the at least one biomedical feature.Type: ApplicationFiled: February 1, 2022Publication date: August 4, 2022Applicant: PathAI, Inc.Inventors: Jason Ku WANG, Maryam POURYAHYA, Kenneth K. LEIDAL, Oscar M. CARRASCO-ZEVALLOS, Ilan WAPINSKI, Amaro TAYLOR-WEINER
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Patent number: 11195279Abstract: In some aspects, the described systems and methods provide for a method for training a statistical model to predict tissue characteristics for a pathology image. The method includes accessing annotated pathology images. Each of the images includes an annotation describing a tissue characteristic category for a portion of the image. A set of training patches and a corresponding set of annotations are defined using an annotated pathology image. Each of the training patches in the set includes values obtained from a respective subset of pixels in the annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the respective subset of pixels. The statistical model is trained based on the set of training patches and the corresponding set of patch annotations. The trained statistical model is stored on at least one storage device.Type: GrantFiled: April 7, 2020Date of Patent: December 7, 2021Assignee: PathAI, Inc.Inventors: Andrew H. Beck, Aditya Khosla
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Patent number: 11017532Abstract: In some aspects, the described systems and methods provide for a method for training a model to predict survival time for a patient. The method includes accessing annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.Type: GrantFiled: April 23, 2020Date of Patent: May 25, 2021Assignee: PathAI, Inc.Inventors: Andrew H. Beck, Aditya Khosla
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Patent number: 10650520Abstract: In some aspects, the described systems and methods provide for a method for training a statistical model to predict tissue characteristics for a pathology image. The method includes accessing annotated pathology images. Each of the images includes an annotation describing a tissue characteristic category for a portion of the image. A set of training patches and a corresponding set of annotations are defined using an annotated pathology image. Each of the training patches in the set includes values obtained from a respective subset of pixels in the annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the respective subset of pixels. The statistical model is trained based on the set of training patches and the corresponding set of patch annotations. The trained statistical model is stored on at least one storage device.Type: GrantFiled: June 6, 2018Date of Patent: May 12, 2020Assignee: PathAI, Inc.Inventors: Andrew H. Beck, Aditya Khosla
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Patent number: 10650929Abstract: In some aspects, the described systems and methods provide for a method for training a model to predict survival time for a patient. The method includes accessing annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.Type: GrantFiled: June 6, 2018Date of Patent: May 12, 2020Assignee: PathAI, Inc.Inventors: Andrew H. Beck, Aditya Khosla