论文标题
3D颅内动脉瘤分类和通过无监督的双分支学习进行分割
3D Intracranial Aneurysm Classification and Segmentation via Unsupervised Dual-branch Learning
论文作者
论文摘要
如今，颅内动脉瘤很常见，并且如何智能检测它们在数字健康中具有重要意义。尽管大多数现有的深度学习研究都以监督方式着重于医学图像，但我们引入了一种基于3D点云数据检测颅内动脉瘤的无监督方法。特别是，我们的方法包括两个阶段：无监督的预训练和下游任务。至于前者，主要想法是将每个点云与其抖动的对应物配对并最大化其信件。然后，我们设计一个双分支对比网络，每个分支的编码器和随后的共同投影头。至于后者，我们为监督分类和细分培训设计了简单的网络。公共数据集（Intra Intra）的实验表明，与某些最先进的监督技术相比，我们的无监督方法具有可比性甚至更好的性能，并且在检测动脉瘤血管时最为突出。 ModelNet40上的实验还表明，我们的方法达到了90.79 \％的准确性，这表现优于现有的最新无监督模型。
Intracranial aneurysms are common nowadays and how to detect them intelligently is of great significance in digital health. While most existing deep learning research focused on medical images in a supervised way, we introduce an unsupervised method for the detection of intracranial aneurysms based on 3D point cloud data. In particular, our method consists of two stages: unsupervised pre-training and downstream tasks. As for the former, the main idea is to pair each point cloud with its jittered counterpart and maximise their correspondence. Then we design a dual-branch contrastive network with an encoder for each branch and a subsequent common projection head. As for the latter, we design simple networks for supervised classification and segmentation training. Experiments on the public dataset (IntrA) show that our unsupervised method achieves comparable or even better performance than some state-of-the-art supervised techniques, and it is most prominent in the detection of aneurysmal vessels. Experiments on the ModelNet40 also show that our method achieves the accuracy of 90.79\% which outperforms existing state-of-the-art unsupervised models.
