3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis

• URL: http://arxiv.org/abs/2112.04863v1
• Date: Thu, 9 Dec 2021 12:31:28 GMT
• Title: 3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis
• Authors: Jianhui Yu, Chaoyi Zhang, Heng Wang, Dingxin Zhang, Yang Song, Tiange Xiang, Dongnan Liu, Weidong Cai
• Abstract summary: We propose an attention-based model specifically for medical point clouds, namely 3D medical point Transformer (3DMedPT) By augmenting contextual information and summarizing local responses at query, our attention module can capture both local context and global content feature interactions. Experiments conducted on IntrA dataset proves the superiority of 3DMedPT, where we achieve the best classification and segmentation results.
• Score: 21.934221178688116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 will be released soon.

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