Correction to: Scientific Reports https://doi.org/10.1038/s41598-019-54548-6, published online 03 December 2019
This Article contains errors in references 5, 26, 36, 37 and 40, which were incorrectly given as:
Hayit, G., Bram van, G. & Ronald, M. S. Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Transactions on Medical Imaging. 35, 1153–1159 (2016).
Kilian, H. et al. Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification, https://doi.org/10.1101/293126 (2018).
Diederik, P. K. & Jimmy, B. Adam: A Method for Stochastic Optimization. In: ICLR. 2015 (2015).
Christian, S., Vincent, V., Sergey, L. & Zbigniew, W. Rethinking the Inception Architecture for Computer Vision. In: CVPR, https://doi.org/10.1109/CVPR.2016.308 (2016).
Loffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In ICML. 448–456 (2015).
References
Greenspan, H., van Ginneken, B. & Summers, R. M. Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Transactions on Medical Imaging. 35, 1153–1159 (2016).
Hett, K. et al. Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification. Sci. Rep. 9, 13845, https://doi.org/10.1038/s41598-019-49970-9 (2019).
Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. In: ICLR. (2015).
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the Inception Architecture for Computer Vision. In: CVPR, https://doi.org/10.1109/CVPR.2016.308 (2016).
Ioffe, S. & Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In: ICML. 448–456 (2015).
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Oh, K., Chung, YC., Kim, K.W. et al. Author Correction: Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network and Transfer Learning. Sci Rep 10, 5663 (2020). https://doi.org/10.1038/s41598-020-62490-1
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DOI: https://doi.org/10.1038/s41598-020-62490-1
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