Print Email Facebook Twitter Multi-view Contour-constrained Transformer Network for Thin-cap Fibroatheroma Identification Title Multi-view Contour-constrained Transformer Network for Thin-cap Fibroatheroma Identification Author Liu, Sijie (Xi’an Jiaotong University; Erasmus MC) Xin, Jingmin (Xi’an Jiaotong University) Wu, Jiayi (Xi’an Jiaotong University) Deng, Yangyang (Xi’an Jiaotong University) Su, Ruisheng (Erasmus MC) Niessen, W.J. (TU Delft ImPhys/Vos group; TU Delft ImPhys/Computational Imaging; Erasmus MC) Zheng, Nanning (Xi’an Jiaotong University) van Walsum, T. (Erasmus MC) Date 2023 Abstract Identification and detection of thin-cap fibroatheroma (TCFA) from intravascular optical coherence tomography (IVOCT) images is critical for treatment of coronary heart diseases. Recently, deep learning methods have shown promising successes in TCFA identification. However, most methods usually do not effectively utilize multi-view information or incorporate prior domain knowledge. In this paper, we propose a multi-view contour-constrained transformer network (MVCTN) for TCFA identification in IVOCT images. Inspired by the diagnosis process of cardiologists, we use contour constrained self-attention modules (CCSM) to emphasize features corresponding to salient regions (i.e., vessel walls) in an unsupervised manner and enhance the visual interpretability based on class activation mapping (CAM). Moreover, we exploit transformer modules (TM) to build global-range relations between two views (i.e., polar and Cartesian views) to effectively fuse features at multiple feature scales. Experimental results on a semi-public dataset and an in-house dataset demonstrate that the proposed MVCTN outperforms other single-view and multi-view methods. Lastly, the proposed MVCTN can also provide meaningful visualization for cardiologists via CAM. Subject IVOCTMulti-view learningPlaque identificationTCFATransformer To reference this document use: http://resolver.tudelft.nl/uuid:79c9a98a-1adc-4dba-9c07-128263d512f6 DOI https://doi.org/10.1016/j.neucom.2022.12.041 ISSN 0925-2312 Source Neurocomputing, 523, 224-234 Part of collection Institutional Repository Document type journal article Rights © 2023 Sijie Liu, Jingmin Xin, Jiayi Wu, Yangyang Deng, Ruisheng Su, W.J. Niessen, Nanning Zheng, T. van Walsum Files PDF 1_s2.0_S0925231222015491_main.pdf 1.92 MB Close viewer /islandora/object/uuid:79c9a98a-1adc-4dba-9c07-128263d512f6/datastream/OBJ/view