Journal
AUTOMATIC CONTROL AND COMPUTER SCIENCES
Volume 54, Issue 6, Pages 560-571Publisher
PLEIADES PUBLISHING INC
DOI: 10.3103/S0146411620060024
Keywords
medical image; word embedding; multi-attention; para-CNNs; IndRNN
Categories
Funding
- National Natural Science Foundation of China (NSFC) [61765014]
- Reserve Talents Project of National High-level Personnel of Special Support Program [QN2016YX0324, Xinjiang [2014]22]
- Urumqi Science and Technology Project [P161310002, Y161010025]
Ask authors/readers for more resources
In order to solve the problems of low gray scale contrast and blurred organ boundaries in some medical images, we proposed a joint algorithm of Multi-Attention Parallel CNNs and Independent Recurrent Neural Networks (MACIR) with word embedding technique combined. First, the word embedding technique is used to map the sparse spatial relation matrix into a real dense vector, which is combined with gray scale and edge matrix as input features. The multi -attention mechanism is used to add weight information to capture the importance of each feature more sensitive. Then, the Parallel Convolutional Neural Networks are used to fully exploit the deep semantic information, and IndRNN is introduced to avoid the loss of pixel hierarchy information and realize the integration of information flow. Finally, the Softmax classifier is used to complete the medical image segmentation task. Experiments showed that word embedding MACIR algorithm could effectively improve the segmentation performance of medical images on the data sets of lung X-ray and cervical CT images.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available