论文标题
超声检查中乳腺癌诊断的相关性对比度学习的蒙版视频建模
Masked Video Modeling with Correlation-aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound
论文作者
论文摘要
乳腺癌是女性癌症死亡的主要原因之一。作为乳房筛查的主要输出，乳房超声（US）视频包含用于癌症诊断的独家动态信息。但是，视频分析的培训模型并非平凡，因为它需要一个大量的数据集，这对注释也很昂贵。此外，乳房病变的诊断面临着独特的挑战，例如阶层间相似性和类内变异。在本文中，我们提出了一种开创性的方法，该方法直接利用了计算机辅助乳腺癌诊断中的视频。它利用掩盖的视频建模作为预处理，以减少对数据集大小和详细注释的依赖。此外，开发了相关性的对比损失，以促进良性病变和恶性病变之间的内部和外部关系的识别。实验结果表明，我们提出的方法实现了有希望的分类性能，并且可以胜过其他最先进的方法。
Breast cancer is one of the leading causes of cancer deaths in women. As the primary output of breast screening, breast ultrasound (US) video contains exclusive dynamic information for cancer diagnosis. However, training models for video analysis is non-trivial as it requires a voluminous dataset which is also expensive to annotate. Furthermore, the diagnosis of breast lesion faces unique challenges such as inter-class similarity and intra-class variation. In this paper, we propose a pioneering approach that directly utilizes US videos in computer-aided breast cancer diagnosis. It leverages masked video modeling as pretraining to reduce reliance on dataset size and detailed annotations. Moreover, a correlation-aware contrastive loss is developed to facilitate the identifying of the internal and external relationship between benign and malignant lesions. Experimental results show that our proposed approach achieved promising classification performance and can outperform other state-of-the-art methods.
