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COVID-19 Image Data Collection
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This paper describes the initial COVID-19 open image data collection. It was created by assembling medical images from websites and publications and currently contains 123 frontal view X-rays.
Forward citations
Cited by 4 Pith papers
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Edge-Based Learning for Improved Classification Under Adversarial Noise
Training on Canny edge maps improves robustness to FGSM adversarial noise compared to training on raw images, though retraining on clean plus noisy raw images recovers more accuracy.
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Lung Disease Detection with Vision Transformers: A Comparative Study of Machine Learning Methods
A Vision Transformer on chest X-rays is claimed to reach 97.83% accuracy, but the reported validation accuracy and missing baselines do not support this claim.
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Toward Multi-Modal Deep Learning for Pulmonary Disease Classification: A Texture-Based Machine Learning Pilot Study on Public Chest X-Ray Data
On 668 public chest X-rays, SVM with HOG/GLCM features achieves 75.4% accuracy (AUC 0.755) for COVID-19 vs other pneumonia, modestly above the 71.6% majority baseline.
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Improving Medical Diagnostics with Vision-Language Models: Convex Hull-Based Uncertainty Analysis
A convex hull area over BERT embeddings of chest X-ray reports is proposed as an uncertainty metric, and the paper shows it grows with sampling temperature in LLM-CXR.
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