RT Journal Article
T1 Multi-task learning with Multi-view Weighted Fusion Attention for artery-specific calcification analysis
A1 Zhang, Weiwei
A1 Yang, Guang
A1 Zhang, Nan
A1 Xu, Lei
A1 Wang, Xiaoqing
A1 Zhang, Yanping
A1 Zhang, Heye
A1 Del Ser, Javier
A1 de Albuquerque, Victor Hugo C.
AB In general, artery-specific calcification analysis comprises the simultaneous calcification segmentation and quantification tasks. It can help provide a thorough assessment for calcification of different coronary arteries, and further allow for an efficient and rapid diagnosis of cardiovascular diseases (CVD). However, as a high-dimensional multi-type estimation problem, artery-specific calcification analysis has not been profoundly investigated due to the intractability of obtaining discriminative feature representations. In this work, we propose a Multi-task learning network with Multi-view Weighted Fusion Attention (MMWFAnet) to solve this challenging problem. The MMWFAnet first employs a Multi-view Weighted Fusion Attention (MWFA) module to extract discriminative feature representations by enhancing the collaboration of multiple views. Specifically, MWFA weights these views to improve multi-view learning for calcification features. Based on the fusion of these multiple views, the proposed approach takes advantage of multi-task learning to obtain accurate segmentation and quantification of artery-specific calcification simultaneously. We perform experimental studies on 676 non-contrast Computed Tomography scans, achieving state-of-the-art performance in terms of multiple evaluation metrics. These compelling results evince that the proposed MMWFAnet is capable of improving the effectivity and efficiency of clinical CVD diagnosis.
SN 1566-2535
YR 2021
FD 2021-07
LA eng
NO Zhang , W , Yang , G , Zhang , N , Xu , L , Wang , X , Zhang , Y , Zhang , H , Del Ser , J & de Albuquerque , V H C 2021 , ' Multi-task learning with Multi-view Weighted Fusion Attention for artery-specific calcification analysis ' , Information Fusion , vol. 71 , pp. 64-76 . https://doi.org/10.1016/j.inffus.2021.01.009
NO Publisher Copyright: © 2021 Elsevier B.V.
NO J. Del Ser receives funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government, as well as from the ELKARTEK and EMAITEK funding programs of the Department of Economic Development and Infrastructures of the same institution. V. H. C. de Albuquerque receives support from the Brazilian National Council for Research and Development (CNPq, Grants #304315/2017-6 and #430274/2018-1 ). L. Xu receives support from a grant from the National Natural Science Foundation of China ( U1908211 ), a grant from the Capital Medical Development Research Foundation of China ( PXM2020_026272_000013 ), and a grant from the National Key Research and Development Program of China ( 2016YFC1300300 ). H. Zhang receives funding support from the National Natural Science Foundation of China (Grants U1801265 and 61771464 ), as well as a grant from the Key Program for International Cooperation Projects of Guangdong Province ( 2018A050506031 ), a grant from the Key-Area Research and Development Program of Guangdong Province ( 2019B010110001 ), the Fundamental Research Funds for 73 the Central Universities ( 19lgzd36 ), and a grant from Guangdong Natural Science Funds for Distinguished Young Scholar ( 2019B151502031 ). G. Yang receives support from the European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combatting Coronavirus Infections Award ‘DRAGON: rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics’ (H2020-JTI-IMI2 101005122), and the AI for Health Imaging Award ‘CHAIMELEON: Accelerating the Lab to Market Transition of AI Tools for Cancer Management’ (H2020-SC1-FA-DTS-2019-1 952172).
DS TECNALIA Publications
RD 28 sept 2024