论文标题
超声视频中乳腺病变检测的新数据集和基线模型
A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos
论文作者
论文摘要
超声检查中的乳腺病变检测对于乳腺癌诊断至关重要。现有方法主要依赖于单个2D超声图像或将未标记的视频和标记为2D图像训练模型进行乳腺病变检测。在本文中，我们首先收集并注释一个超声视频数据集（188个视频），以进行乳腺病变检测。此外，我们通过汇总视频级别的病变分类功能和剪贴画级的时间功能来解决超声视频中乳房病变检测的解决剪辑级和视频级特征聚合网络（CVA-NET）。剪辑级时空特征编码有序视频框架的本地时间信息以及洗牌视频帧的全局时间信息。在我们的CVA-NET中，设计了一个Inter-Video融合模块，以融合原始视频框架的本地功能以及从洗牌视频帧中的全局功能，并设计了一个内部视频融合模块，以学习相邻视频框架之间的时间信息。此外，我们学习视频水平功能，以将原始视频的乳房病变分类为良性或恶性病变，以进一步增强超声视频中最终的乳房病变检测性能。我们注释的数据集的实验结果表明，我们的CVA-NET明显优于最先进的方法。相应的代码和数据集可在\ url {https://github.com/jhl-det/cva-net}上公开获得。
Breast lesion detection in ultrasound is critical for breast cancer diagnosis. Existing methods mainly rely on individual 2D ultrasound images or combine unlabeled video and labeled 2D images to train models for breast lesion detection. In this paper, we first collect and annotate an ultrasound video dataset (188 videos) for breast lesion detection. Moreover, we propose a clip-level and video-level feature aggregated network (CVA-Net) for addressing breast lesion detection in ultrasound videos by aggregating video-level lesion classification features and clip-level temporal features. The clip-level temporal features encode local temporal information of ordered video frames and global temporal information of shuffled video frames. In our CVA-Net, an inter-video fusion module is devised to fuse local features from original video frames and global features from shuffled video frames, and an intra-video fusion module is devised to learn the temporal information among adjacent video frames. Moreover, we learn video-level features to classify the breast lesions of the original video as benign or malignant lesions to further enhance the final breast lesion detection performance in ultrasound videos. Experimental results on our annotated dataset demonstrate that our CVA-Net clearly outperforms state-of-the-art methods. The corresponding code and dataset are publicly available at \url{https://github.com/jhl-Det/CVA-Net}.
