# AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray

arXiv: https://arxiv.org/abs/2105.09937 · HTML: https://mlsanity.com/paper/2105.09937/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 36 references that are in the map.
- First posted on arXiv 20 May 2021.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models](https://mlsanity.com/paper/1802.08773/) (2018)
- [Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation](https://mlsanity.com/paper/1805.08298/) (2018)
- [TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays](https://mlsanity.com/paper/1801.04334/) (2018)
- [GraphGAN: Graph Representation Learning with Generative Adversarial Nets](https://mlsanity.com/paper/1711.08267/) (2017)
- [ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rays](https://mlsanity.com/paper/1904.00325/) (2019)
- [Deep Learning for Automatic Pneumonia Detection](https://mlsanity.com/paper/2005.13899/) (2020)
- [Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer](https://mlsanity.com/paper/1712.07632/) (2017)
- [Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays](https://mlsanity.com/paper/1807.00958/) (2018)
- [Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels](https://mlsanity.com/paper/2002.11629/) (2020)

## Built on by (most cited first, 19 of 19)

- [Chest ImaGenome Dataset for Clinical Reasoning](https://mlsanity.com/paper/2108.00316/) (2021)
- [Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding](https://mlsanity.com/paper/2210.03416/) (2022)
- [CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays](https://mlsanity.com/paper/2208.03873/) (2022)
- [Anatomy-Driven Pathology Detection on Chest X-rays](https://mlsanity.com/paper/2309.02578/) (2023)
- [Robust Asymmetric Loss for Multi-Label Long-Tailed Learning](https://mlsanity.com/paper/2308.05542/) (2023)
- [Self adaptive global-local feature enhancement for radiology report generation](https://mlsanity.com/paper/2211.11380/) (2022)
- [Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting](https://mlsanity.com/paper/2308.15961/) (2023)
- [FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rays](https://mlsanity.com/paper/2203.15723/) (2022)
- [Anatomical grounding pre-training for medical phrase grounding](https://mlsanity.com/paper/2502.16585/) (2025)
- [Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and Interpretation](https://mlsanity.com/paper/2411.04607/) (2024)
- [Encoder-Decoder Based Long Short-Term Memory (LSTM) Model for Video Captioning](https://mlsanity.com/paper/2401.02052/) (2023)
- [Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities](https://mlsanity.com/paper/2403.18196/) (2024)
- [Improving Chest X-Ray Classification by RNN-based Patient Monitoring](https://mlsanity.com/paper/2210.16074/) (2022)
- [CLARiTy: A Vision Transformer for Multi-Label Classification and Weakly-Supervised Localization of Chest X-ray Pathologies](https://mlsanity.com/paper/2512.16700/) (2025)
- [Spatially-Preserving Flattening for Location-Aware Classification of Findings in Chest X-Rays](https://mlsanity.com/paper/2204.09676/) (2022)
- [Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification](https://mlsanity.com/paper/2307.15987/) (2023)
- [Artificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images](https://mlsanity.com/paper/2503.00657/) (2025)
- [Clinically-Grounded Hierarchical Classification for Consistent Chest X-ray Interpretation](https://mlsanity.com/paper/2608.03016/) (2026)
- [CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs](https://mlsanity.com/paper/2608.28137/) (2026)

## Abstract (arXiv)

Radiologists usually observe anatomical regions of chest X-ray images as well as the overall image before making a decision. However, most existing deep learning models only look at the entire X-ray image for classification, failing to utilize important anatomical information. In this paper, we propose a novel multi-label chest X-ray classification model that accurately classifies the image finding and also localizes the findings to their correct anatomical regions. Specifically, our model consists of two modules, the detection module and the anatomical dependency module. The latter utilizes graph convolutional networks, which enable our model to learn not only the label dependency but also the relationship between the anatomical regions in the chest X-ray. We further utilize a method to efficiently create an adjacency matrix for the anatomical regions using the correlation of the label across the different regions. Detailed experiments and analysis of our results show the effectiveness of our method when compared to the current state-of-the-art multi-label chest X-ray image classification methods while also providing accurate location information.