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公开(公告)号：US11527319B1
公开(公告)日：2022-12-13
申请号：US17019142
申请日：2020-09-11
申请人： PathAI, Inc.
发明人： 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
摘要： 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.
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公开(公告)号：US11915823B1
公开(公告)日：2024-02-27
申请号：US17984866
申请日：2022-11-10
申请人： PathAI, Inc.
发明人： 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
摘要： 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.
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3.发明公开公开(公告)号：US20230326022A1
公开(公告)日：2023-10-12
申请号：US18297281
申请日：2023-04-07
发明人： 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
CPC分类号： G06T7/0012 , G06V20/698 , G06V10/774 , G06V10/82 , G06V20/695 , G16H30/40 , G16H50/20 , G16H20/10 , G06T2207/20036 , G06T2207/20081 , G06T2207/20084 , G06V2201/03 , G06T2207/30096
摘要： 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.
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