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DETECTING AND CLASSIFYING MEDICAL IMAGES BASED ON CONTINUOUSLY-LEARNING WHOLE BODY LANDMARKS DETECTIONS

机译:基于连续学习的整体地标检测的医学图像检测和分类

摘要

A computer-implemented method for automatically generating metadata tags for a medical image includes receiving a medical image and automatically identifying a set of body landmarks in the medical image using one or more machine learning models. A set of rules are applied to the set of body landmarks to identify anatomical objects present in the image. As an alternative to using the set of rules, in some embodiments, one or more machine learning models to the set of body landmarks to identify anatomical objects present in the image. Once the anatomical objects are identified, metadata tags corresponding to the anatomical objects are generated and stored in the medical image. Then, the medical image with the metadata tags is transferred to a data repository.
机译:一种用于自动生成医学图像的元数据标签的计算机实现的方法,包括:接收医学图像并使用一个或多个机器学习模型自动识别医学图像中的一组身体界标。一组规则应用于该组身体标志,以识别图像中存在的解剖对象。作为使用该组规则的替代,在一些实施例中,一个或多个机器学习模型对该组身体地标进行识别以识别图像中存在的解剖对象。一旦识别出解剖对象,就生成与解剖对象相对应的元数据标签并将其存储在医学图像中。然后,将带有元数据标签的医学图像传输到数据存储库。

著录项

  • 公开/公告号US2019057501A1

    专利类型

  • 公开/公告日2019-02-21

    原文格式PDF

  • 申请/专利权人 SIEMENS HEALTHCARE GMBH;

    申请/专利号US201816019579

  • 申请日2018-06-27

  • 分类号G06T7/00;A61B5/055;G06T7/11;G16H30/40;G06N99/00;G06K9/62;

  • 国家 US

  • 入库时间 2022-08-21 12:06:33

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