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Poplin, R., Varadarajan, A.V., Blumer, K., Liu, Y., McConnell, M.V., Corrado, G.S., et al. (2018) Prediction of Cardiovascular Risk Factors from Retinal Fundus Photographs via Deep Learning. Nature Biomedical Engineering, 2, 158-164.
https://doi.org/10.1038/s41551-018-0195-0

has been cited by the following article:

• TITLE: Effect of the Pixel Interpolation Method for Downsampling Medical Images on Deep Learning Accuracy

  AUTHORS: Daisuke Hirahara, Eichi Takaya, Mizuki Kadowaki, Yasuyuki Kobayashi, Takuya Ueda

  KEYWORDS: Downsampling, Interpolation, Deep Learning, Convolutional Neural Networks, Medical Images, Nearest Neighbor, Bilinear, Hamming Window, Bicubic, Lanczos

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.11, November 30, 2021

  ABSTRACT: Background: High-resolution medical images often need to be downsampled because of the memory limitations of the hardware used for machine learning. Although various image interpolation methods are applicable to downsampling, the effect of data preprocessing on the learning performance of convolutional neural networks (CNNs) has not been fully investigated. Methods: In this study, five different pixel interpolation algorithms (nearest neighbor, bilinear, Hamming window, bicubic, and Lanczos interpolation) were used for image downsampling to investigate their effects on the prediction accuracy of a CNN. Chest X-ray images from the NIH public dataset were examined by downsampling 10 patterns. Results: The accuracy improved with a decreasing image size, and the best accuracy was achieved at 64 × 64 pixels. Among the interpolation methods, bicubic interpolation obtained the highest accuracy, followed by the Hamming window.