TY - GEN
T1 - ASDNet
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
AU - Nie, Dong
AU - Gao, Yaozong
AU - Wang, Li
AU - Shen, Dinggang
N1 - Funding Information:
D. Shen—This work was supported by the National Institutes of Health grant 1R01 CA140413.
PY - 2018
Y1 - 2018
N2 - Segmentation is a key step for various medical image analysis tasks. Recently, deep neural networks could provide promising solutions for automatic image segmentation. The network training usually involves a large scale of training data with corresponding ground truth label maps. However, it is very challenging to obtain the ground-truth label maps due to the requirement of expertise knowledge and also intensive labor work. To address such challenges, we propose a novel semi-supervised deep learning framework, called “Attention based Semi-supervised Deep Networks” (ASDNet), to fulfill the segmentation tasks in an end-to-end fashion. Specifically, we propose a fully convolutional confidence network to adversarially train the segmentation network. Based on the confidence map from the confidence network, we then propose a region-attention based semi-supervised learning strategy to include the unlabeled data for training. Besides, sample attention mechanism is also explored to improve the network training. Experimental results on real clinical datasets show that our ASDNet can achieve state-of-the-art segmentation accuracy. Further analysis also indicates that our proposed network components contribute most to the improvement of performance.
AB - Segmentation is a key step for various medical image analysis tasks. Recently, deep neural networks could provide promising solutions for automatic image segmentation. The network training usually involves a large scale of training data with corresponding ground truth label maps. However, it is very challenging to obtain the ground-truth label maps due to the requirement of expertise knowledge and also intensive labor work. To address such challenges, we propose a novel semi-supervised deep learning framework, called “Attention based Semi-supervised Deep Networks” (ASDNet), to fulfill the segmentation tasks in an end-to-end fashion. Specifically, we propose a fully convolutional confidence network to adversarially train the segmentation network. Based on the confidence map from the confidence network, we then propose a region-attention based semi-supervised learning strategy to include the unlabeled data for training. Besides, sample attention mechanism is also explored to improve the network training. Experimental results on real clinical datasets show that our ASDNet can achieve state-of-the-art segmentation accuracy. Further analysis also indicates that our proposed network components contribute most to the improvement of performance.
UR - http://www.scopus.com/inward/record.url?scp=85053839687&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-00937-3_43
DO - 10.1007/978-3-030-00937-3_43
M3 - Conference contribution
AN - SCOPUS:85053839687
SN - 9783030009366
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 370
EP - 378
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Frangi, Alejandro F.
A2 - Fichtinger, Gabor
A2 - Schnabel, Julia A.
A2 - Alberola-López, Carlos
A2 - Davatzikos, Christos
PB - Springer Verlag
Y2 - 16 September 2018 through 20 September 2018
ER -