Instance importance-Aware graph convolutional network for 3D medical diagnosis

Automatic diagnosis of 3D medical data is a significant goal of intelligent healthcare. By exploiting the abundant pathological information of 3D data, human experts and algorithms can provide accurate predictions for patients. Considering the high cost of collecting exhaustive annotations for 3D data, a sustainable alternative is to develop diagnosis algorithms with merely patient-level labels. Motivated by the fact that 2D slices of 3D data hold explicit diagnostic efficacy, we propose the Instance Importance-aware Graph Convolutional Network (I²GCN) under the multi-instance learning (MIL). Specifically, we first calculate the instance importance of each slice towards diagnosis using a preliminary MIL classifier, which is further utilized to promote the refined diagnosis branch. In the refined diagnosis branch, we devise the Instance Importance-aware Graph Convolutional Layer (I²GCLayer) to exploit complementary features in both importance-based and feature-based topologies. Moreover, to alleviate the deficient supervision of 3D dataset, we propose the importance-based Sub-Graph Augmentation (SGA) to effectively regularize the framework training. Extensive experiments confirm the effectiveness of our method with different organs and modals on the CC-CCII and PROSTATEx datasets, which outperforms state-of-the-art methods by a large margin. The source code is available at https://github.com/CityU-AIM-Group/I2GCN.