| 发明名称 |
Method and system for approximating deep neural networks for anatomical object detection |
| 摘要 |
A method and system for approximating a deep neural network for anatomical object detection is discloses. A deep neural network is trained to detect an anatomical object in medical images. An approximation of the trained deep neural network is calculated that reduces the computational complexity of the trained deep neural network. The anatomical object is detected in an input medical image of a patient using the approximation of the trained deep neural network. |
| 申请公布号 |
US9633306(B2) |
申请公布日期 |
2017.04.25 |
| 申请号 |
US201514706108 |
申请日期 |
2015.05.07 |
| 申请人 |
Siemens Healthcare GmbH |
发明人 |
Liu David;Lay Nathan;Zhou Shaohua Kevin;Kretschmer Jan;Nguyen Hien;Singh Vivek Kumar;Zheng Yefeng;Georgescu Bogdan;Comaniciu Dorin |
| 分类号 |
G06N3/08;G06K9/00;G06T7/00 |
主分类号 |
G06N3/08 |
| 代理机构 |
|
代理人 |
|
| 主权项 |
1. A method for anatomical object detection in a medical image comprising:
training a deep neural network to detect the anatomical object in medical images; calculating an approximation of the trained deep neural network that reduces the computational complexity of the trained deep neural network; and detecting the anatomical object in a received medical image of a patient using the approximation of the trained deep neural network, wherein calculating an approximation of the trained deep neural network that reduces the computational complexity of the trained deep neural network comprises: for each of a plurality of nodes in each of a plurality of layers of the trained deep neural network, reconstructing a trained weight matrix for the node using 1-D Haar wavelet bases and wavelet coefficients. |
| 地址 |
Erlangen DE |
