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A-line-based thin-cap fibroatheroma detection with multi-view IVOCT images using multi-task learning and contrastive learning

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Abstract

Automatic detection of thin-cap fibroatheroma (TCFA) is essential to prevent acute coronary syndrome. Hence, in this paper, a method is proposed to detect TCFAs by directly classifying each A-line using multi-view intravascular optical coherence tomography (IVOCT) images. To solve the problem of false positives, a multi-input–output network was developed to implement image-level classification and A-line-based classification at the same time, and a contrastive consistency term was designed to ensure consistency between two tasks. In addition, to learn spatial and global information and obtain the complete extent of TCFAs, an architecture and a regional connectivity constraint term are proposed to classify each A-line of IVOCT images. Experimental results obtained on the 2017 China Computer Vision Conference IVOCT dataset show that the proposed method achieved state-of-art performance with a total score of $88.7 \pm 0.88\%$, overlap rate of $88.64 \pm 0.26\%$, precision rate of $84.34 \pm 0.86\%$, and recall rate of $93.67 \pm 2.29\%$.

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Data availability

Data underlying the results presented in this paper were publicly available for the 2017 China Computer Vision Conference Vulnerable Plaque (CCCV-VP) detection challenge, and are not publicly available at this time but may be obtained from the sponsor upon reasonable request.

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