mlsanityData to 7 Oct 2026

A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images

arXiv 2004.12823 ↗First posted 27 Apr 2020eess.IV cs.CV cs.LGcited by 28

Not on the map. Why?

Abstract (arXiv)

In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature. We show that similar results can be obtained using X-Ray images that do not contain most of the lungs. We are able to remove the lungs from the images by turning to black the center of the X-Ray scan and training our classifiers only on the outer part of the images. Hence, we deduce that several testing protocols for the recognition are not fair and that the neural networks are learning patterns in the dataset that are not correlated to the presence of COVID-19. Finally, we show that creating a fair testing protocol is a challenging task, and we provide a method to measure how fair a specific testing protocol is. In the future research we suggest to check the fairness of a testing protocol using our tools and we encourage researchers to look for better techniques than the ones that we propose.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Apr 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 9; 2023: 2; 2024: 1; 2025: 1; 2026: 1.

28 papers.

Sort:

  1. 2021EnD: Entangling and Disentangling deep representations for bias correctioncited by 105
  2. 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
  3. 2021EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use casecited by 30
  4. 2021Towards Robust Classification Model by Counterfactual and Invariant Data Generationcited by 30
  5. 2021A Systematic Collection of Medical Image Datasets for Deep Learningcited by 29
  6. 2022Improving deep neural network generalization and robustness to background bias via layer-wise relevance propagation optimizationcited by 25
  7. 2021Self-supervised deep convolutional neural network for chest X-ray classificationcited by 16
  8. 2022Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approachescited by 15
  9. 2022A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicroncited by 13
  10. 2022Vision Transformers in Medical Imaging: A Reviewcited by 8
  11. 2021COVID-19 Modeling: A Reviewcited by 7
  12. 2021COVID-19 detection using chest X-rays: is lung segmentation important for generalization?cited by 6
16 more
  1. 2022BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Datacited by 4
  2. 2021Deep survival analysis with longitudinal X-rays for COVID-19cited by 3
  3. 2021Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentationcited by 3
  4. 2022Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification Systemcited by 2
  5. 2023A general-purpose AI assistant embedded in an open-source radiology information systemcited by 2
  6. 2021CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Imagescited by 1
  7. 2024Faster ISNet for Background Bias Mitigation on Deep Neural Networkscited by 1
  8. 2021Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classificationcited by 0
  9. 2021Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray datacited by 0
  10. 2021Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasetscited by 0
  11. 2022POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 Detectioncited by 0
  12. 2022Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcarecited by 0
  13. 2022Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Imagescited by 0
  14. 2023A design of Convolutional Neural Network model for the Diagnosis of the COVID-19cited by 0
  15. 2025Open-source framework for detecting bias and overfitting for large pathology imagescited by 0
  16. 2026Are Data Augmentation and Segmentation Always Necessary? Insights from COVID-19 X-Rays and a Methodology Thereofcited by 0