Shi Peiwen, Xin Jingmin, Wu Jiayi, Deng Yangyang, Cai Zhuotong, Du Shaoyi, Zheng Nanning
Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.
Cardiovascular Department, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
J Biophotonics. 2023 May;16(5):e202200343. doi: 10.1002/jbio.202200343. Epub 2023 Feb 2.
Automatic detection of thin-cap fibroatheroma (TCFA) on intravascular optical coherence tomography images is essential for the prevention of acute coronary syndrome. However, existing methods need to mark the exact location of TCFAs on each frame as supervision, which is extremely time-consuming and expensive. Hence, a new weakly supervised framework is proposed to detect TCFAs using only image-level tags as supervision. The framework comprises cut, feature extraction, relation, and detection modules. First, based on prior knowledge, a cut module was designed to generate a small number of specific region proposals. Then, to learn global information, a relation module was designed to learn the spatial adjacency and order relationships at the feature level, and an attention-based strategy was introduced in the detection module to effectively aggregate the classification results of region proposals as the image-level predicted score. The results demonstrate that the proposed method surpassed the state-of-the-art weakly supervised detection methods.
在血管内光学相干断层扫描图像上自动检测薄帽纤维粥样斑块（TCFA）对于预防急性冠状动脉综合征至关重要。然而，现有方法需要将TCFAs在每一帧上的精确位置标记为监督信息，这极其耗时且成本高昂。因此，提出了一种新的弱监督框架，仅使用图像级标签作为监督来检测TCFAs。该框架由切割、特征提取、关系和检测模块组成。首先，基于先验知识设计了一个切割模块，以生成少量特定区域提议。然后，为了学习全局信息，设计了一个关系模块来学习特征级别的空间邻接和顺序关系，并在检测模块中引入了基于注意力的策略，以有效地聚合区域提议的分类结果作为图像级预测分数。结果表明，所提出的方法超越了当前最先进的弱监督检测方法。
