# Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1804.07839 · HTML: https://mlsanity.com/paper/1804.07839/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 20 of 20)

- [Improving Radiology Report Generation Systems by Removing Hallucinated References to Non-existent Priors](https://mlsanity.com/paper/2210.06340/) (2022)
- [Multi-view analysis of unregistered medical images using cross-view transformers](https://mlsanity.com/paper/2103.11390/) (2021)
- [CheXFusion: Effective Fusion of Multi-View Features using Transformers for Long-Tailed Chest X-Ray Classification](https://mlsanity.com/paper/2308.03968/) (2023)
- [Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images](https://mlsanity.com/paper/2211.14552/) (2022)
- [Cardiovascular Disease Detection from Multi-View Chest X-rays with BI-Mamba](https://mlsanity.com/paper/2405.18533/) (2024)
- [Multi-View Hypercomplex Learning for Breast Cancer Screening](https://mlsanity.com/paper/2204.05798/) (2022)
- [Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning](https://mlsanity.com/paper/2306.00003/) (2023)
- [Nodule detection and generation on chest X-rays: NODE21 Challenge](https://mlsanity.com/paper/2401.02192/) (2024)
- [Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment](https://mlsanity.com/paper/2104.05261/) (2021)
- [HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images](https://mlsanity.com/paper/2310.06143/) (2023)
- [Searching for Pneumothorax in X-Ray Images Using Autoencoded Deep Features](https://mlsanity.com/paper/2102.06096/) (2021)
- [Image Embedding and Model Ensembling for Automated Chest X-Ray Interpretation](https://mlsanity.com/paper/2105.02966/) (2021)
- [Explaining the Black-box Smoothly- A Counterfactual Approach](https://mlsanity.com/paper/2101.04230/) (2021)
- [Improving Chest X-Ray Classification by RNN-based Patient Monitoring](https://mlsanity.com/paper/2210.16074/) (2022)
- [Cross-Fundus Transformer for Multi-modal Diabetic Retinopathy Grading with Cataract](https://mlsanity.com/paper/2411.00726/) (2024)
- [A distillation based approach for the diagnosis of diseases](https://mlsanity.com/paper/2108.03470/) (2021)
- [Chest X-Rays Image Classification from beta-Variational Autoencoders Latent Features](https://mlsanity.com/paper/2109.14760/) (2021)
- [Cardiomegaly Detection using Deep Convolutional Neural Network with U-Net](https://mlsanity.com/paper/2205.11515/) (2022)
- [Early Diagnosis of Chronic Obstructive Pulmonary Disease from Chest X-Rays using Transfer Learning and Fusion Strategies](https://mlsanity.com/paper/2211.06925/) (2022)
- [Multi Head Attention Enhanced Inception v3 for Cardiomegaly Detection](https://mlsanity.com/paper/2511.20101/) (2025)

## Abstract (arXiv)

The MIMIC-CXR dataset is (to date) the largest released chest x-ray dataset consisting of 473,064 chest x-rays and 206,574 radiology reports collected from 63,478 patients. We present the results of training and evaluating a collection of deep convolutional neural networks on this dataset to recognize multiple common thorax diseases. To the best of our knowledge, this is the first work that trains CNNs for this task on such a large collection of chest x-ray images, which is over four times the size of the largest previously released chest x-ray corpus (ChestX-Ray14). We describe and evaluate individual CNN models trained on frontal and lateral CXR view types. In addition, we present a novel DualNet architecture that emulates routine clinical practice by simultaneously processing both frontal and lateral CXR images obtained from a radiological exam. Our DualNet architecture shows improved performance in recognizing findings in CXR images when compared to applying separate baseline frontal and lateral classifiers.