# Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays

arXiv: https://arxiv.org/abs/2002.02582 · HTML: https://mlsanity.com/paper/2002.02582/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 16 of 16)

- [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)
- [Multi-view analysis of unregistered medical images using cross-view transformers](https://mlsanity.com/paper/2103.11390/) (2021)
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- [Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning](https://mlsanity.com/paper/2306.00003/) (2023)
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- [Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images](https://mlsanity.com/paper/2109.11480/) (2021)
- [Deep learning for understanding multilabel imbalanced Chest X-ray datasets](https://mlsanity.com/paper/2207.14408/) (2022)
- [Knowledge Distillation approach towards Melanoma Detection](https://mlsanity.com/paper/2210.08086/) (2022)
- [Any-to-Any Vision-Language Model for Multimodal X-ray Imaging and Radiological Report Generation](https://mlsanity.com/paper/2505.01091/) (2025)
- [A data- and compute-efficient chest X-ray foundation model beyond aggressive scaling](https://mlsanity.com/paper/2602.22843/) (2026)
- [StudyFormer : Attention-Based and Dynamic Multi View Classifier for X-ray images](https://mlsanity.com/paper/2302.11840/) (2023)
- [Cross-Fundus Transformer for Multi-modal Diabetic Retinopathy Grading with Cataract](https://mlsanity.com/paper/2411.00726/) (2024)
- [Designing a Robust Radiology Report Generation System](https://mlsanity.com/paper/2411.01153/) (2024)
- [Random Token Fusion for Multi-View Medical Diagnosis](https://mlsanity.com/paper/2410.15847/) (2024)

## Abstract (arXiv)

Most deep learning models in chest X-ray prediction utilize the posteroanterior (PA) view due to the lack of other views available. PadChest is a large-scale chest X-ray dataset that has almost 200 labels and multiple views available. In this work, we use PadChest to explore multiple approaches to merging the PA and lateral views for predicting the radiological labels associated with the X-ray image. We find that different methods of merging the model utilize the lateral view differently. We also find that including the lateral view increases performance for 32 labels in the dataset, while being neutral for the others. The increase in overall performance is comparable to the one obtained by using only the PA view with twice the amount of patients in the training set.