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

IPC分类号： G06K9/00 ,  G16H30/40 ,  G16H30/20 ,  G06N3/08 ,  G06K9/62 ,  G16H50/20

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.

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

IPC分类号： G06K9/00 ,  G16H30/40 ,  G16H30/20 ,  G06N3/08 ,  G16H50/20 ,  G06F18/23

CPC分类号： G16H30/40 ,  G06F18/23 ,  G06N3/08 ,  G16H30/20 ,  G16H50/20

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.