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

•We present an Instance Importance-aware Graph Convolutional Network (I2GCN) to achieve 3D medical diagnosis with merely patient-level supervision, which enables the multi-instance learning (MIL) framework to exploit the relationship among instances comprehensively.•We evaluate the diagnostic importance of each instance by revisiting the channelwise contribution of embeddings, which can be utilized to perform the refined diagnosis. To the best of our knowledge, this work represents the first attempt to estimate the instance importance from the supervised knowledge in MIL.•In the refined diagnosis branch, we propose the Instance Importance-aware Graph Convolutional Layer (I2GCLayer) to explore the complementary information with the importance-based and feature-based topologies under the orthogonal constraint. Besides, the importance-based Sub-Graph Augmentation (SGA) is devised to enhance the graph-based learning compatible with MIL.•We perform extensive experiments of 3D medical diagnosis on publicly available lung CT and prostate MRI datasets to validate the effectiveness of the proposed I2GCN and instance importance calculation thoroughly. The proposed I2GCN outperforms state-of-the-art methods by a large margin. [Display omitted] 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 org