3D Intracranial Aneurysm Classification and Segmentation via Unsupervised Dual-branch Learning
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 29 of its 50 references that are in the map. First posted on arXiv 6 Jan 2022.
Builds on
29 of 50 references.
References · 29
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
- 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
- 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
- 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,838
- 2018Deep Graph Infomaxcited by 1,101
- 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 733
- 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
- 2020PointContrast: Unsupervised Pre-training for 3D Point Cloud Understandingcited by 502
- 2018SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filterscited by 261
17 more
- 2018SO-Net: Self-Organizing Network for Point Cloud Analysiscited by 210
- 2016Learning a Predictable and Generative Vector Representation for Objectscited by 173
- 20183D Point Capsule Networkscited by 106
- 2019Unsupervised Multi-Task Feature Learning on Point Cloudscited by 89
- 2020Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learningcited by 68
- 2019Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Predictioncited by 66
- 2016VConv-DAE: Deep Volumetric Shape Learning Without Object Labelscited by 50
- 2020Pointfilter: Point Cloud Filtering via Encoder-Decoder Modelingcited by 50
- 2019PointHop: An Explainable Machine Learning Method for Point Cloud Classificationcited by 46
- 2020IntrA: 3D Intracranial Aneurysm Dataset for Deep Learningcited by 41
- 2020P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understandingcited by 41
- 2021Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Cloudscited by 23
- 20213D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysiscited by 23
- 2021Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoningcited by 22
- 2017Learning Representations and Generative Models for 3D Point Cloudscited by 21
- 2020MedMeshCNN -- Enabling MeshCNN for Medical Surface Modelscited by 5
- 2021Deep Point Cloud Normal Estimation via Triplet Learningcited by 4
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