# Do Lateral Views Help Automated Chest X-ray Predictions?

arXiv: https://arxiv.org/abs/1904.08534 · HTML: https://mlsanity.com/paper/1904.08534/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [Exploring scalable medical image encoders beyond text supervision](https://mlsanity.com/paper/2401.10815/) (2024)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data](https://mlsanity.com/paper/2405.12833/) (2024)
- [StudyFormer : Attention-Based and Dynamic Multi View Classifier for X-ray images](https://mlsanity.com/paper/2302.11840/) (2023)
- [VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imaging](https://mlsanity.com/paper/2505.15248/) (2025)

## Abstract (arXiv)

Most convolutional neural networks in chest radiology use only the frontal posteroanterior (PA) view to make a prediction. However the lateral view is known to help the diagnosis of certain diseases and conditions. The recently released PadChest dataset contains paired PA and lateral views, allowing us to study for which diseases and conditions the performance of a neural network improves when provided a lateral x-ray view as opposed to a frontal posteroanterior (PA) view. Using a simple DenseNet model, we find that using the lateral view increases the AUC of 8 of the 56 labels in our data and achieves the same performance as the PA view for 21 of the labels. We find that using the PA and lateral views jointly doesn't trivially lead to an increase in performance but suggest further investigation.