Impact of Gold-Standard Label Errors on Evaluating Performance of Deep Learning Models in Diabetic Retinopathy Screening: Nationwide Real-World Validation Study.

Yueye Wang Xiaotong Han Cong Li Lixia Luo Qiuxia Yin Jian Zhang Guankai Peng Danli Shi Mingguang He

Published in: Journal of medical Internet research (2024)

Label errors based on human image grading, although in a small percentage, can significantly affect the performance evaluation of DL algorithms in real-world DR screening.

Keyphrases

deep learning
diabetic retinopathy
machine learning
optical coherence tomography
endothelial cells
artificial intelligence
patient safety
convolutional neural network
adverse drug
emergency department
induced pluripotent stem cells
cross sectional
quality improvement
silver nanoparticles
drug induced