The Effect of Image Resolution on Deep Learning in Radiography

Abstract

Increasing image resolution for CNN training often has a trade-off with the maximum possible batch size, yet optimal selection of image resolution has the potential for further increasing neural network performance for various radiology-based machine learning tasks. Furthermore, identifying diagnosis-specific tasks that require relatively higher image resolution can potentially provide insight into the relative difficulty of identifying different radiology findings. <i>Supplemental material is available for this article.</i> © RSNA, 2020See also the commentary by Lakhani in this issue.