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Document type:
Konferenzbeitrag 
Author(s):
Baur, C.; Albarqouni, S.; Navab, N. 
Title:
Semi-Supervised Learning for Fully Convolutional Networks 
Abstract:
Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there is n...    »
 
Keywords:
miccai,MRI,MSLesion,Embedding,FCN,deeplearning 
Book / Congress title:
International Conference on Medical Image Computing and Computer-Assisted Intervention 
Organization:
Springer 
Year:
2017 
Pages:
311--319