# ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rays

arXiv: https://arxiv.org/abs/1904.00325 · HTML: https://mlsanity.com/paper/1904.00325/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2019.

## Built on by (most cited first, 22 of 22)

- [Graph Representation Learning in Biomedicine](https://mlsanity.com/paper/2104.04883/) (2021)
- [AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray](https://mlsanity.com/paper/2105.09937/) (2021)
- [Chest X-ray Image Classification: A Causal Perspective](https://mlsanity.com/paper/2305.12072/) (2023)
- [Missing-modality Enabled Multi-modal Fusion Architecture for Medical Data](https://mlsanity.com/paper/2309.15529/) (2023)
- [SHINE: SubHypergraph Inductive Neural nEtwork](https://mlsanity.com/paper/2210.07309/) (2022)
- [SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification](https://mlsanity.com/paper/2311.07750/) (2023)
- [MURPHY: Relations Matter in Surgical Workflow Analysis](https://mlsanity.com/paper/2212.12719/) (2022)
- [Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty](https://mlsanity.com/paper/2305.05344/) (2023)
- [Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance](https://mlsanity.com/paper/2306.01232/) (2023)
- [Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation](https://mlsanity.com/paper/2311.15213/) (2023)
- [Instrumental Variable Learning for Chest X-ray Classification](https://mlsanity.com/paper/2305.12070/) (2023)
- [Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification](https://mlsanity.com/paper/2406.02990/) (2024)
- [Distribution Preserving Graph Representation Learning](https://mlsanity.com/paper/2202.13428/) (2022)
- [Machine Learning Applications in Lung Cancer Diagnosis, Treatment and Prognosis](https://mlsanity.com/paper/2203.02794/) (2022)
- [Machine learning's own Industrial Revolution](https://mlsanity.com/paper/2311.02278/) (2023)
- [Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical Images](https://mlsanity.com/paper/2311.07321/) (2023)
- [Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning](https://mlsanity.com/paper/2410.17494/) (2024)
- [Artificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images](https://mlsanity.com/paper/2503.00657/) (2025)
- [CheX-DS: Improving Chest X-ray Image Classification with Ensemble Learning Based on DenseNet and Swin Transformer](https://mlsanity.com/paper/2505.11168/) (2025)
- [ModAn-MulSupCon: Modality-and Anatomy-Aware Multi-Label Supervised Contrastive Pretraining for Medical Imaging](https://mlsanity.com/paper/2508.18613/) (2025)
- [Momentum-Anchored Multi-Scale Fusion Model for Long-Tailed Chest X-Ray Classification](https://mlsanity.com/paper/2605.02292/) (2026)
- [Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection](https://mlsanity.com/paper/2609.10988/) (2026)

## Abstract (arXiv)

Image representation is a fundamental task in computer vision. However, most of the existing approaches for image representation ignore the relations between images and consider each input image independently. Intuitively, relations between images can help to understand the images and maintain model consistency over related images, leading to better explainability. In this paper, we consider modeling the image-level relations to generate more informative image representations, and propose ImageGCN, an end-to-end graph convolutional network framework for inductive multi-relational image modeling. We apply ImageGCN to chest X-ray images where rich relational information is available for disease identification. Unlike previous image representation models, ImageGCN learns the representation of an image using both its original pixel features and its relationship with other images. Besides learning informative representations for images, ImageGCN can also be used for object detection in a weakly supervised manner. The experimental results on 3 open-source x-ray datasets, ChestX-ray14, CheXpert and MIMIC-CXR demonstrate that ImageGCN can outperform respective baselines in both disease identification and localization tasks and can achieve comparable and often better results than the state-of-the-art methods.