14
B. Solano-Rojas et al.
5. Cohen, J.P., Bertin, P., Frappier, V.: Chester: A Web Delivered Locally Computed
Chest X-Ray Disease Prediction System. arXiv:1901.11210 (2019)
6. Cui, R., Liu, M.: Hippocampus analysis by combination of 3-D DenseNet and
shapes for Alzheimer’s disease diagnosis. IEEE J. Biomed. Health Inform. 23(5),
2099–2107 (2019). https://doi.org/10.1109/JBHI.2018.2882392
7. Graber, M., Franklin, N.: Diagnostic error in internal medicine. Arch. Intern. Med.
165 (2005). https://doi.org/10.1001/archinte.165.13.1493
8. Hara, K., Kataoka, H., Satoh, Y.: Can spatiotemporal 3D CNNs retrace the history
of 2D CNNs and ImageNet? In: Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), pp. 6546–6555 (2018)
9. He, G., Ping, A., Wang, X., Zhu, Y.: Alzheimer’s disease diagnosis model based on
three-dimensional full convolutional DenseNet. In: 2019 10th International Conference on Information Technology in Medicine and Education (ITME), pp. 13–17,
August 2019. https://doi.org/10.1109/ITME.2019.00014
10. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected
convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 2261–2269 (2017). https://doi.org/
10.1109/CVPR.2017.243
11. Jabason, E., Ahmad, M.O., Swamy, M.N.S.: Classification of Alzheimer’s disease
from MRI data using an ensemble of hybrid deep convolutional neural networks.
In: 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems
(MWSCAS), pp. 481–484, August 2019. https://doi.org/10.1109/MWSCAS.2019.
8884939
12. Khan, S., Rahmani, H., Shah, S., Bennamoun, M.: A Guide to Convolutional Neural Networks for Computer Vision. Synthesis Lectures on Computer
Vision, No. 1. Morgan & Claypool Publishers (2018). https://doi.org/10.2200/
S00822ED1V01Y201712COV015
13. Kollias, D., Tagaris, A., Stafylopatis, A., Kollias, S., Tagaris, G.: Deep neural architectures for prediction in healthcare. Complex Intell. Syst. 4(2), 119–131 (2017).
https://doi.org/10.1007/s40747-017-0064-6
14. Lee, C.S., Nagy, P.G., Weaver, S.J., Newman-Toker, D.E.: Cognitive and system
factors contributing to diagnostic errors in radiology. Am. J. Roentgenol. 201(3),
611–617 (2013). https://doi.org/10.2214/AJR.12.10375
15. National Institute of Biomedical Imaging and Bioengineering: Magnetic Resonance Imaging (MRI). https://www.nibib.nih.gov/science-education/sciencetopics/magnetic-resonance-imaging-mri
16. National Institute of Biomedical Imaging and Bioengineering: Nuclear Medicine.
https://www.nibib.nih.gov/science-education/science-topics/nuclear-medicine#
pid-1001
17. National Institute on Aging: Alzheimer’s Disease Fact Sheet. https://www.nia.nih.
gov/health/alzheimers-disease-fact-sheet. Accessed 13 May 2018
18. Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest Xrays with deep learning. CoRR abs/1711.05225 (2017). http://arxiv.org/abs/1711.
05225
19. Tagaris, A., Kollias, D., Stafylopatis, A., Tagaris, G., Kollias, S.: Machine learning for neurodegenerative disorder diagnosis — survey of practices and launch of
benchmark dataset. Int. J. Artif. Intell. Tools 27(03), 1850011 (2018). https://doi.
org/10.1142/S0218213018500112
B. Solano-Rojas et al.
5. Cohen, J.P., Bertin, P., Frappier, V.: Chester: A Web Delivered Locally Computed
Chest X-Ray Disease Prediction System. arXiv:1901.11210 (2019)
6. Cui, R., Liu, M.: Hippocampus analysis by combination of 3-D DenseNet and
shapes for Alzheimer’s disease diagnosis. IEEE J. Biomed. Health Inform. 23(5),
2099–2107 (2019). https://doi.org/10.1109/JBHI.2018.2882392
7. Graber, M., Franklin, N.: Diagnostic error in internal medicine. Arch. Intern. Med.
165 (2005). https://doi.org/10.1001/archinte.165.13.1493
8. Hara, K., Kataoka, H., Satoh, Y.: Can spatiotemporal 3D CNNs retrace the history
of 2D CNNs and ImageNet? In: Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), pp. 6546–6555 (2018)
9. He, G., Ping, A., Wang, X., Zhu, Y.: Alzheimer’s disease diagnosis model based on
three-dimensional full convolutional DenseNet. In: 2019 10th International Conference on Information Technology in Medicine and Education (ITME), pp. 13–17,
August 2019. https://doi.org/10.1109/ITME.2019.00014
10. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected
convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 2261–2269 (2017). https://doi.org/
10.1109/CVPR.2017.243
11. Jabason, E., Ahmad, M.O., Swamy, M.N.S.: Classification of Alzheimer’s disease
from MRI data using an ensemble of hybrid deep convolutional neural networks.
In: 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems
(MWSCAS), pp. 481–484, August 2019. https://doi.org/10.1109/MWSCAS.2019.
8884939
12. Khan, S., Rahmani, H., Shah, S., Bennamoun, M.: A Guide to Convolutional Neural Networks for Computer Vision. Synthesis Lectures on Computer
Vision, No. 1. Morgan & Claypool Publishers (2018). https://doi.org/10.2200/
S00822ED1V01Y201712COV015
13. Kollias, D., Tagaris, A., Stafylopatis, A., Kollias, S., Tagaris, G.: Deep neural architectures for prediction in healthcare. Complex Intell. Syst. 4(2), 119–131 (2017).
https://doi.org/10.1007/s40747-017-0064-6
14. Lee, C.S., Nagy, P.G., Weaver, S.J., Newman-Toker, D.E.: Cognitive and system
factors contributing to diagnostic errors in radiology. Am. J. Roentgenol. 201(3),
611–617 (2013). https://doi.org/10.2214/AJR.12.10375
15. National Institute of Biomedical Imaging and Bioengineering: Magnetic Resonance Imaging (MRI). https://www.nibib.nih.gov/science-education/sciencetopics/magnetic-resonance-imaging-mri
16. National Institute of Biomedical Imaging and Bioengineering: Nuclear Medicine.
https://www.nibib.nih.gov/science-education/science-topics/nuclear-medicine#
pid-1001
17. National Institute on Aging: Alzheimer’s Disease Fact Sheet. https://www.nia.nih.
gov/health/alzheimers-disease-fact-sheet. Accessed 13 May 2018
18. Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest Xrays with deep learning. CoRR abs/1711.05225 (2017). http://arxiv.org/abs/1711.
05225
19. Tagaris, A., Kollias, D., Stafylopatis, A., Tagaris, G., Kollias, S.: Machine learning for neurodegenerative disorder diagnosis — survey of practices and launch of
benchmark dataset. Int. J. Artif. Intell. Tools 27(03), 1850011 (2018). https://doi.
org/10.1142/S0218213018500112