3D Intracranial Aneurysm Classification and Segmentation via Unsupervised Dual-branch Learning. (arXiv:2201.02198v1 [eess.IV])

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.

Source: https://arxiv.org/abs/2201.02198


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