Papers › 3D Medical Point Transformer: Introducing Convolution to Attention Networks for...
3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis
Jianhui Yu, Chaoyi Zhang, Heng Wang, Dingxin Zhang, Yang song, Tiange Xiang, Dongnan Liu, Weidong Cai
General point clouds have been increasingly investigated for different tasks, and recently Transformer-based networks are proposed for point cloud analysis. However, there are barely related works for medical point clouds, which are important for disease detection and treatment. In this work, we propose an attention-based model specifically for medical point clouds, namely 3D medical point Transformer (3DMedPT), to examine the complex biological structures. By augmenting contextual information and summarizing local responses at query, our attention module can capture both local context and global content feature interactions. However, the insufficient training samples of medical data may lead to poor feature learning, so we apply position embeddings to learn accurate local geometry and Multi-Graph Reasoning (MGR) to examine global knowledge propagation over channel graphs to enrich feature representations. Experiments conducted on IntrA dataset proves the superiority of 3DMedPT, where we achieve the best classification and segmentation results. Furthermore, the promising generalization ability of our method is validated on general 3D point cloud benchmarks: ModelNet40 and ShapeNetPart. Code is released.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Part Segmentation | IntrA | 3DMedPT | DSC (A) | 89.71 | #1 of 7 | Archive leaderboard | report |
| 3D Part Segmentation | IntrA | 3DMedPT | DSC (V) | 97.29 | #1 of 7 | Archive leaderboard | report |
| 3D Part Segmentation | IntrA | 3DMedPT | IoU (A) | 82.39 | #1 of 7 | Archive leaderboard | report |
| 3D Part Segmentation | IntrA | 3DMedPT | IoU (V) | 94.82 | #1 of 7 | Archive leaderboard | report |
| 3D Point Cloud Classification | IntrA | 3DMedPT | F1 score (5-fold) | 0.936 | #1 of 12 | Archive leaderboard | report |
| 3D Point Cloud Classification | ModelNet40 | 3DMedPT | Number of params | 1.54M | #63 of 111 | Archive leaderboard | report |
| 3D Point Cloud Classification | ModelNet40 | 3DMedPT | Overall Accuracy | 93.4 | #63 of 111 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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