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64 条记录 (耗时 0.028 秒)

    AUTOMATIC TRAINING AND DEPLOYMENT OF DEEP LEARNING TECHNOLOGIES
    45.
    发明申请
    AUTOMATIC TRAINING AND DEPLOYMENT OF DEEP LEARNING TECHNOLOGIES 有权

    公开(公告)号：US20210182674A1

    公开(公告)日：2021-06-17

    申请号：US17118817

    申请日：2020-12-11

    申请人： Siemens Healthcare GmbH

    发明人： Axel Avedis Petit ,  Guillaume Chabin ,  Eli Gibson ,  Sasa Grbic ,  Dorin Comaniciu

    IPC分类号： G06N3/08 ,  G06K9/62 ,  G06F8/60

    摘要： Systems and methods for automatically training a machine learning based model are provided. A trigger for automatically training a machine learning based model is received. In response to receiving the trigger, a preprocessing manager for executing preprocessing code for preprocessing training data is automatically invoked. A training manager for executing training code for training the machine learning based model based on the preprocessed training data is automatically invoked. A deployment manager for executing deployment code for converting the trained machine learning based model to a production model is automatically invoked. The production model is output.

    COPD CLASSIFICATION WITH MACHINE-TRAINED ABNORMALITY DETECTION
    47.
    发明申请
    COPD CLASSIFICATION WITH MACHINE-TRAINED ABNORMALITY DETECTION 审中-公开

    公开(公告)号：US20200265276A1

    公开(公告)日：2020-08-20

    申请号：US16275780

    申请日：2019-02-14

    申请人： Siemens Healthcare GmbH

    发明人： Zhoubing Xu ,  Shikha Chaganti ,  Sasa Grbic

    IPC分类号： G06K9/66 ,  G06K9/00 ,  G06T11/00 ,  G06T7/00 ,  A61B6/03 ,  A61B6/00 ,  A61B5/00 ,  A61B5/085

    摘要： For COPD classification in a medical imaging system, machine learning is used to learn to classify whether a patient has COPD. An image-to-image network deep learns spatial features indicative of various or any type of COPD. The pulmonary function test may be used as the ground truth in training the features and classification from the spatial features. Due to the high availability of pulmonary function test results and corresponding CT scans, there are many training samples. Values from learned features of the image-to-image network are then used to create a spatial distribution of level of COPD, providing information useful for distinguishing between types of COPD without requiring ground truth annotation of spatial distribution of COPD in the training.

    Hierarchical analysis of medical images for identifying and assessing lymph nodes
    50.
    发明授权
    Hierarchical analysis of medical images for identifying and assessing lymph nodes 有权

    公开(公告)号：US11514571B2

    公开(公告)日：2022-11-29

    申请号：US16714031

    申请日：2019-12-13

    申请人： Siemens Healthcare GmbH

    发明人： Bogdan Georgescu ,  Elijah D. Bolluyt ,  Alexandra Comaniciu ,  Sasa Grbic

    IPC分类号： G06T7/11 ,  G06T7/00 ,  G06T11/00 ,  A61B5/00

    摘要： Systems and methods for identifying and assessing lymph nodes are provided. Medical image data (e.g., one or more computed tomography images) of a patient is received and anatomical landmarks in the medical image data are detected. Anatomical objects are segmented from the medical image data based on the one or more detected anatomical landmarks. Lymph nodes are identified in the medical image data based on the one or more detected anatomical landmarks and the one or more segmented anatomical objects. The identified lymph nodes may be assessed by segmenting the identified lymph nodes from the medical image data and quantifying the segmented lymph nodes. The identified lymph nodes and/or the assessment of the identified lymph nodes are output.