@Article{cmc.2020.07968,
AUTHOR = {Chao Luo, Canghong Shi, Xiaojie Li, Xin Wang, Yucheng Chen, Dongrui Gao, Youbing Yin, Qi Song, Xi Wu, Jiliu Zhou},
TITLE = {Multi-Task Learning Using Attention-Based Convolutional  Encoder-Decoder for Dilated Cardiomyopathy CMR Segmentation and Classification},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {995--1012},
URL = {http://www.techscience.com/cmc/v63n2/38556},
ISSN = {1546-2226},
ABSTRACT = {Myocardial segmentation and classification play a major role in the diagnosis 
of cardiovascular disease. Dilated Cardiomyopathy (DCM) is a kind of common chronic 
and life-threatening cardiopathy. Early diagnostics significantly increases the chances of 
correct treatment and survival. However, accurate and rapid diagnosis of DCM is still 
challenge due to high variability of cardiac structure, low contrast cardiac magnetic 
resonance (CMR) images, and intrinsic noise in synthetic CMR images caused by motion 
artifact and cardiac dynamics. Moreover, visual assessment and empirical evaluation are 
widely used in routine clinical diagnosis, but they are subject to high inter-observer 
variability and are both subjective and non-reproducible. To solve this problem, we 
proposed an effective unified multi-task framework for dilated cardiomyopathy CMR 
segmentation and classification simultaneously, and we firstly update one independent 
encoder from both recovery decoder and parallel attention path sharing some partial 
weights. This can encode both task choices into good embedding, but each one can 
achieve significant improvements respectively from the given embedding. It consists of 
three branches: extraction path, attention path, and recovery path, which allows the model 
to learn more higher-level intermediate representations and makes a more accurate 
prediction. We validated our approach on a DCM dataset, which contains 1155 CMR 
LGE images. Experimental results show that our multi-task network has achieved 
accuracy of 97.63%, AUC of 98.32%, demonstrating effectively segmenting the 
myocardium, quickly and accurately diagnosing the presence or absence of dilation.},
DOI = {10.32604/cmc.2020.07968}
}