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Intracranial aneurysms are common nowadays and how to detect themintelligently is of great significance in digital health. While most existingdeep learning research focused on medical images in a supervised way, weintroduce an unsupervised method for the detection of intracranial aneurysmsbased on 3D point cloud data. In particular, our method consists of two stages:unsupervised pre-training and downstream tasks. As for the former, the mainidea is to pair each point cloud with its jittered counterpart and maximisetheir correspondence. Then we design a dual-branch contrastive network with anencoder for each branch and a subsequent common projection head. As for thelatter, we design simple networks for supervised classification andsegmentation training. Experiments on the public dataset (IntrA) show that ourunsupervised method achieves comparable or even better performance than somestate-of-the-art supervised techniques, and it is most prominent in thedetection of aneurysmal vessels. Experiments on the ModelNet40 also show thatour method achieves the accuracy of 90.79\% which outperforms existingstate-of-the-art unsupervised models.

3D Intracranial Aneurysm Classification and Segmentation via Unsupervised Dual-branch Learning