Huang Xiaoyang, Lin Zhi, Huang Shaohui, Wang Fu Lee, Chan Moon-Tong, Wang Liansheng
Department of Computer Science, School of Informatics, Xiamen University, Xiamen, China.
School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, Hong Kong SAR, China.
Front Oncol. 2022 Oct 24;12:952457. doi: 10.3389/fonc.2022.952457. eCollection 2022.
Breast cancer is the most common cause of cancer death in women. Early screening and treatment can effectively improve the success rate of treatment. Ultrasound imaging technology, as the preferred modality for breast cancer screening, provides an essential reference for early diagnosis. Existing computer-aided ultrasound imaging diagnostic techniques mainly rely on the selected key frames for breast cancer lesion diagnosis. In this paper, we first collected and annotated a dataset of ultrasound video sequences of 268 cases of breast lesions. Moreover, we propose a contrastive learning-guided multi-meta attention network (CLMAN) by combining a deformed feature extraction module and a multi-meta attention module to address breast lesion diagnosis in ultrasound sequence. The proposed feature extraction module can autonomously acquire key information of the feature map in the spatial dimension, whereas the designed multi-meta attention module is dedicated to effective information aggregation in the temporal dimension. In addition, we utilize a contrast learning strategy to alleviate the problem of high imaging variability within ultrasound lesion videos. The experimental results on our collected dataset show that our CLMAN significantly outperforms existing advanced methods for video classification.
乳腺癌是女性癌症死亡的最常见原因。早期筛查和治疗可有效提高治疗成功率。超声成像技术作为乳腺癌筛查的首选方式，为早期诊断提供了重要参考。现有的计算机辅助超声成像诊断技术主要依靠所选关键帧进行乳腺癌病变诊断。在本文中，我们首先收集并标注了一个包含268例乳腺病变超声视频序列的数据集。此外，我们通过结合变形特征提取模块和多元注意力模块，提出了一种对比学习引导的多元注意力网络（CLMAN），以解决超声序列中的乳腺病变诊断问题。所提出的特征提取模块能够在空间维度上自主获取特征图的关键信息，而设计的多元注意力模块则致力于在时间维度上进行有效信息聚合。此外，我们利用对比学习策略来缓解超声病变视频中成像变异性高的问题。在我们收集的数据集上的实验结果表明，我们的CLMAN明显优于现有的先进视频分类方法。
