Multi-task learning with Multi-view Weighted Fusion Attention for artery-specific calcification analysis
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2021-07
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Abstract
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.
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Publisher Copyright: © 2021 Elsevier B.V.
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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