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.

本篇文章提出了一种特定于医用点云的基于注意力机制的 3D 医用点云 Transformer (3DMedPT) 模型，通过增加上下文信息和在查询时总结局部响应，可以捕捉局部和全局特征交互，同时应用位置嵌入学习准确的局部几何形状和多图推理 (MGR) 来丰富特征表示，在医用数据上的实验表明该方法的分类和分割能力优于其他方法，并且具有良好的泛化能力。