# Deep Learning Models May Spuriously Classify Covid-19 from X-ray Images Based on Confounders

arXiv: https://arxiv.org/abs/2102.04300 · HTML: https://mlsanity.com/paper/2102.04300/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 40 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [COVID-19 Image Data Collection](https://mlsanity.com/paper/2003.11597/) (2020)
- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (2020)
- [BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients](https://mlsanity.com/paper/2006.01174/) (2020)
- [Deep Learning COVID-19 Features on CXR using Limited Training Data Sets](https://mlsanity.com/paper/2004.05758/) (2020)
- [COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images](https://mlsanity.com/paper/2003.11055/) (2020)
- [Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network](https://mlsanity.com/paper/2003.13815/) (2020)
- [COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs](https://mlsanity.com/paper/2003.14395/) (2020)
- [Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays](https://mlsanity.com/paper/2004.08379/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach](https://mlsanity.com/paper/2004.10641/) (2020)
- [Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset](https://mlsanity.com/paper/2004.02060/) (2020)
- [Deep Learning for Screening COVID-19 using Chest X-Ray Images](https://mlsanity.com/paper/2004.10507/) (2020)
- [Extracting possibly representative COVID-19 Biomarkers from X-Ray images with Deep Learning approach and image data related to Pulmonary Diseases](https://mlsanity.com/paper/2004.00338/) (2020)
- [DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images](https://mlsanity.com/paper/2004.04582/) (2020)
- [A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays](https://mlsanity.com/paper/2005.01578/) (2020)
- [Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning](https://mlsanity.com/paper/2004.12592/) (2020)
- [A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening](https://mlsanity.com/paper/2004.12786/) (2020)
- [A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2](https://mlsanity.com/paper/2004.08052/) (2020)
- [A cascade network for Detecting COVID-19 using chest x-rays](https://mlsanity.com/paper/2005.01468/) (2020)

## Built on by (most cited first, 1 of 1)

- [Understanding Dataset Bias in Medical Imaging: A Case Study on Chest X-rays](https://mlsanity.com/paper/2507.07722/) (2025)

## Abstract (arXiv)

Identifying who is infected with the Covid-19 virus is critical for controlling its spread. X-ray machines are widely available worldwide and can quickly provide images that can be used for diagnosis. A number of recent studies claim it may be possible to build highly accurate models, using deep learning, to detect Covid-19 from chest X-ray images. This paper explores the robustness and generalization ability of convolutional neural network models in diagnosing Covid-19 disease from frontal-view (AP/PA), raw chest X-ray images that were lung field cropped. Some concerning observations are made about high performing models that have learned to rely on confounding features related to the data source, rather than the patient's lung pathology, when differentiating between Covid-19 positive and negative labels. Specifically, these models likely made diagnoses based on confounding factors such as patient age or image processing artifacts, rather than medically relevant information.