U-Net

U-net 是基于FCN的一个语义分割网络,适合用来做医学图像的分割。

语义分割医学图像卷积神经网络
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contributed at 2020-02-23

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Affiliation:  
+49-(0)761-203-8285
Email:  
olafr@deepmind.com
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Method-focused categoriesData-perspectiveIntelligent computation analysis

Model Description

English {{currentDetailLanguage}} English

Quote from:  https://arxiv.org/abs/1505.04597

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper,Present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. Show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) Won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. 

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How to Cite

Olaf Ronneberger (2020). U-Net, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/65ec0198-9509-4e98-8996-8d0a1bee72e5
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Last modifier : 
Xu Kai
Last modify time : 
2020-12-18
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Contributor(s)

initial contribute: 2020-02-23

Authorship

Affiliation:  
+49-(0)761-203-8285
Email:  
olafr@deepmind.com
Homepage:  
View
Is authorship not correct? Feedback

History

Last modifier : 
Xu Kai
Last modify time : 
2020-12-18
Modify times : 
View History

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