Public Covid-19 X-ray datasets and their impact on model bias – A systematic review of a significant problem

Author:

Garcia Santa Cruz Beatriz,Bossa Matías Nicolás,Sölter Jan,Husch Andreas Dominik

Funder

Fonds National de la Recherche

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference86 articles.

1. Discovery of a generalization gap of convolutional neural networks on covid-19 x-rays classification;Ahmed;IEEE Access,2021

2. Ahmed, K. B., Hall, L. O., Goldgof, D. B., Goldgof, G. M., Paul, R., 2021b. Deep learning models may spuriously classify covid-19 from x-ray images based on confounders. arXiv:2102.04300.

3. Deep transfer learning for COVID-19 prediction: case study for limited data problems;Albahli;Curr. Med. Imaging,2020

4. Diagnostic value of imaging modalities for COVID-19: scoping review;Aljondi;J. Med. Internet Res.,2020

5. Lung ultrasound for point-of-care COVID-19 pneumonia stratification: computer-aided diagnostics in a smartphone. first experiences classifying semiology from public datasets;Almeida,2020

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