# Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels

arXiv: https://arxiv.org/abs/1911.06475 · HTML: https://mlsanity.com/paper/1911.06475/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2019.

## Built on by (most cited first, 8 of 8)

- [GAN-based Data Augmentation for Chest X-ray Classification](https://mlsanity.com/paper/2107.02970/) (2021)
- [Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation](https://mlsanity.com/paper/2103.00528/) (2021)
- [A Relational-learning Perspective to Multi-label Chest X-ray Classification](https://mlsanity.com/paper/2103.06220/) (2021)
- [Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification](https://mlsanity.com/paper/2102.13269/) (2021)
- [Image Embedding and Model Ensembling for Automated Chest X-Ray Interpretation](https://mlsanity.com/paper/2105.02966/) (2021)
- [One-shot Weakly-Supervised Segmentation in Medical Images](https://mlsanity.com/paper/2111.10773/) (2021)
- [Rep-GLS: Report-Guided Generalized Label Smoothing for Robust Disease Detection](https://mlsanity.com/paper/2508.02495/) (2025)
- [Chest X-Rays Image Classification from beta-Variational Autoencoders Latent Features](https://mlsanity.com/paper/2109.14760/) (2021)

## Abstract (arXiv)

Chest radiography is one of the most common types of diagnostic radiology exams, which is critical for screening and diagnosis of many different thoracic diseases. Specialized algorithms have been developed to detect several specific pathologies such as lung nodule or lung cancer. However, accurately detecting the presence of multiple diseases from chest X-rays (CXRs) is still a challenging task. This paper presents a supervised multi-label classification framework based on deep convolutional neural networks (CNNs) for predicting the risk of 14 common thoracic diseases. We tackle this problem by training state-of-the-art CNNs that exploit dependencies among abnormality labels. We also propose to use the label smoothing technique for a better handling of uncertain samples, which occupy a significant portion of almost every CXR dataset. Our model is trained on over 200,000 CXRs of the recently released CheXpert dataset and achieves a mean area under the curve (AUC) of 0.940 in predicting 5 selected pathologies from the validation set. This is the highest AUC score yet reported to date. The proposed method is also evaluated on the independent test set of the CheXpert competition, which is composed of 500 CXR studies annotated by a panel of 5 experienced radiologists. The performance is on average better than 2.6 out of 3 other individual radiologists with a mean AUC of 0.930, which ranks first on the CheXpert leaderboard at the time of writing this paper.