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公开(公告)号：US10102482B2
公开(公告)日：2018-10-16
申请号：US14820751
申请日：2015-08-07
Applicant: Google LLC
Inventor： Heng-Tze Cheng , Jeremiah Harmsen , Alexandre Tachard Passos , David Edgar Lluncor , Shahar Jamshy , Tal Shaked , Tushar Deepak Chandra
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a factorization model to learning features of model inputs of a trained model such that the factorization model is predictive of outcome for which the machine learned model is trained.
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公开(公告)号：US11126649B2
公开(公告)日：2021-09-21
申请号：US16032276
申请日：2018-07-11
Applicant: Google LLC
Inventor： Krishnan Eswaran , Shravya Shetty , Daniel Shing Shun Tse , Shahar Jamshy , Zvika Ben-Haim
Abstract: A computer-implemented system is described for identifying and retrieving similar radiology images to a query image. The system includes one or more fetchers receiving the query image and retrieving a set of candidate similar radiology images from a data store. One or more scorers receive the query image and the set of candidate similar radiology images and generate a similarity score between the query image and each candidate image. A pooler receives the similarity scores from the one or more scorers, ranks the candidate images, and returns a list of the candidate images reflecting the ranking. The scorers implement a modelling technique to generate the similarity score capturing a plurality of similarity attributes of the query image and the set of candidate similar radiology images and annotations associated therewith. For example, the similarity attributes could be patient, diagnostic and/or visual similarity, and the modelling techniques could be triplet loss, classification loss, regression loss and object detection loss.
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公开(公告)号：US20230169652A1
公开(公告)日：2023-06-01
申请号：US18011888
申请日：2022-05-12
Applicant: Google LLC
Inventor： Sahar Kazemzadeh , Dong Jin Yu , Shahar Jamshy , Rory Pilgrim , Zaid Isam Nabulsi , Andrew Beckmann Sellergren , Yun Liu , Shruthi Prabhakara , Atilla Peter Kiraly
IPC: G06T7/00 , G06N3/0464 , G06N3/084 , G16H10/60 , G16H30/40 , G16H50/20 , G16H50/30 , G06T7/11 , A61B6/00
CPC classification number: G06T7/0012 , G06N3/0464 , G06N3/084 , G16H10/60 , G16H30/40 , G16H50/20 , G16H50/30 , G06T7/11 , A61B6/50 , G06T2207/10116 , G06T2207/30061 , G06T2207/20081 , G06T2207/20084
Abstract: Systems and methods for chest condition determination can leverage one or more machine-learned models to process radiograph data to determine risk data (e.g., a preliminary diagnosis). For example, systems and methods can utilize a pathology model to process a chest x-ray to generate a tuberculosis diagnosis. The one or more machine-learned models can segment the lungs, can detect features in the data, and can pool the segmentation and located features to determine the diagnosis.
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公开(公告)号：US20200019617A1
公开(公告)日：2020-01-16
申请号：US16032276
申请日：2018-07-11
Applicant: Google LLC
Inventor： Krishnan Eswaran , Shravya Shetty , Daniel Shing Shun Tse , Shahar Jamshy , Zvika Ben-Haim
Abstract: A computer-implemented system is described for identifying and retrieving similar radiology images to a query image. The system includes one or more fetchers receiving the query image and retrieving a set of candidate similar radiology images from a data store. One or more scorers receive the query image and the set of candidate similar radiology images and generate a similarity score between the query image and each candidate image. A pooler receives the similarity scores from the one or more scorers, ranks the candidate images, and returns a list of the candidate images reflecting the ranking. The scorers implement a modelling technique to generate the similarity score capturing a plurality of similarity attributes of the query image and the set of candidate similar radiology images and annotations associated therewith. For example, the similarity attributes could be patient, diagnostic and/or visual similarity, and the modelling techniques could be triplet loss, classification loss, regression loss and object detection loss.
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