Abstract
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.
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Funding
This work was supported by the National Natural Science Foundation of China (NSFC) (no. 61765014); Reserve Talents Project of National High-level Personnel of Special Support Program (QN2016YX0324); Urumqi Science and Technology Project (nos. P161310002 and Y161010025); and Reserve Talents Project of National High-level Personnel of Special Support Program (Xinjiang [2014]22).
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Junlong Cheng, Tian, S., Yu, L. et al. Multi-Attention Mechanism Medical Image Segmentation Combined with Word Embedding Technology. Aut. Control Comp. Sci. 54, 560–571 (2020). https://doi.org/10.3103/S0146411620060024
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DOI: https://doi.org/10.3103/S0146411620060024