# Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels

arXiv: https://arxiv.org/abs/2005.12734 · HTML: https://mlsanity.com/paper/2005.12734/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

## Built on by (most cited first, 30 of 56)

- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification](https://mlsanity.com/paper/2210.12843/) (2022)
- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis](https://mlsanity.com/paper/2108.05930/) (2021)
- [VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels](https://mlsanity.com/paper/2102.11467/) (2021)
- [EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised Learning](https://mlsanity.com/paper/2405.05237/) (2024)
- [Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis](https://mlsanity.com/paper/2112.12349/) (2021)
- [Label-Assemble: Leveraging Multiple Datasets with Partial Labels](https://mlsanity.com/paper/2109.12265/) (2021)
- [PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children](https://mlsanity.com/paper/2203.10612/) (2022)
- [GAN-based Data Augmentation for Chest X-ray Classification](https://mlsanity.com/paper/2107.02970/) (2021)
- [Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images](https://mlsanity.com/paper/2109.10778/) (2021)
- [Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rays](https://mlsanity.com/paper/2106.05915/) (2021)
- [RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction](https://mlsanity.com/paper/2308.05046/) (2023)
- [Deep learning with noisy labels in medical prediction problems: a scoping review](https://mlsanity.com/paper/2403.13111/) (2024)
- [Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining](https://mlsanity.com/paper/2308.04551/) (2023)
- [Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2108.06486/) (2021)
- [An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph](https://mlsanity.com/paper/2208.03545/) (2022)
- [HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images](https://mlsanity.com/paper/2310.06143/) (2023)
- [Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese](https://mlsanity.com/paper/2209.04794/) (2022)
- [ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification](https://mlsanity.com/paper/2210.02998/) (2022)
- [A clinical validation of VinDr-CXR, an AI system for detecting abnormal chest radiographs](https://mlsanity.com/paper/2104.02256/) (2021)
- [Learning Low-Rank Feature for Thorax Disease Classification](https://mlsanity.com/paper/2404.18933/) (2024)
- [Encoding Hierarchical Information in Neural Networks helps in Subpopulation Shift](https://mlsanity.com/paper/2112.10844/) (2021)
- [Calibrating Histopathology Image Classifiers using Label Smoothing](https://mlsanity.com/paper/2201.11866/) (2022)
- [Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance](https://mlsanity.com/paper/2306.01232/) (2023)
- [Computer-Aided Diagnosis of Thoracic Diseases in Chest X-rays using hybrid CNN-Transformer Architecture](https://mlsanity.com/paper/2404.11843/) (2024)
- [Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook](https://mlsanity.com/paper/2512.09315/) (2025)
- [Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification](https://mlsanity.com/paper/2204.01728/) (2022)
- [Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks](https://mlsanity.com/paper/2208.05433/) (2022)

## Abstract (arXiv)

The chest X-rays (CXRs) is one of the views most commonly ordered by radiologists (NHS),which is critical for diagnosis of many different thoracic diseases. Accurately detecting thepresence of multiple diseases from CXRs is still a challenging task. We present a multi-labelclassification framework based on deep convolutional neural networks (CNNs) for diagnos-ing the presence of 14 common thoracic diseases and observations. Specifically, we trained astrong set of CNNs that exploit dependencies among abnormality labels and used the labelsmoothing regularization (LSR) for a better handling of uncertain samples. Our deep net-works were trained on over 200,000 CXRs of the recently released CheXpert dataset (Irvinandal., 2019) and the final model, which was an ensemble of the best performing networks,achieved a mean area under the curve (AUC) of 0.940 in predicting 5 selected pathologiesfrom the validation set. To the best of our knowledge, this is the highest AUC score yetreported to date. More importantly, the proposed method was also evaluated on an inde-pendent test set of the CheXpert competition, containing 500 CXR studies annotated by apanel of 5 experienced radiologists. The reported performance was on average better than2.6 out of 3 other individual radiologists with a mean AUC of 0.930, which had led to thecurrent state-of-the-art performance on the CheXpert test set.