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The effect of dataset confounding on predictions of deep neural networks for medical imaging
Proceedings of the Northern Lights Deep Learning Workshop
The use of Convolutional Neural Networks (CNN) in medical imaging has often outperformed previous solutions and even specialists, becoming a promising technology for Computer-aidedDiagnosis (CAD) systems. However, recent works suggested that CNN may have poor generalisation on new data, for instance, generated in different hospitals. Uncontrolled confounders have been proposed as a common reason. In this paper, we experimentally demonstrate the impact of confounding data in unknown scenarios.doi:10.7557/18.6302 fatcat:g2fgh7ubfzbi5eoh3rwjg4vm5i