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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number102421
Journal / PublicationMedical Image Analysis
Volume78
Online published18 Mar 2022
Publication statusPublished - May 2022

Abstract

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.

Research Area(s)

  • 3D Medical data, COVID-19 CT, Graph convolution, Multi-instance learning, Prostate MRI