References
-
Barthel H, Gertz HJ, Eresel S, Peters O, Bartenstein P, Buerger K, Hiemeyer F, Wittmer-Rump SM, Seibyl J, Reininger C, Sabri O. Cerebral amyloid-
${\beta}$ PET with florbetaben (18F) in patients with Alzheimer's disease and healthy controls: a multicenter phase 2 diagnostic study. The Lancet Neurology. 2011. 10: 424-435. https://doi.org/10.1016/S1474-4422(11)70077-1 - Bergstra J, Bengio Y. Random search for hyper-parameter optimization. Journal of Machine Learning Research. 2012. 13: 281-305.
- Blanc-Durand P, Van Der Gucht A, Guedj E, Abulizi M, Aoun-Sebaiti M, Lerman L, Verger A, Authier FJ, Itti E. Cerebral 18F-FDG PET in macrophagic myofasciitis: An individual SVM-based approach. PloS One. 2017. 12: e0181152. https://doi.org/10.1371/journal.pone.0181152
- Brucher N, Mandegaran R, Filleron T, Wagner T. Measurement of inter- and intra-observer variability in the routine clinical interpretation of brain 18-FDG PET-CT. Annals of Nuclear Medicine. 2015. 29: 233-239. https://doi.org/10.1007/s12149-014-0932-8
-
Bullich S, Seibyl J, Catafau AM, Jovalekic A, Koglin N, Barthel H, Sabri O, Santi SD. Optimized classification of
$^{18}F$ -Florbetaben PET scans as positive and negative using an SUVR quantitative approach and comparison to visual assessment. NeuroImage Clinical. 2017. 15: 325-332. https://doi.org/10.1016/j.nicl.2017.04.025 - Chaves R, Ramirez J, Gorriz JM, Illan IA, Salas-Gonzalez D. FDG and PIB biomarker PET analysis for the Alzheimer's disease detection using Association Rules. IEEE Nuclear Science Symposium and Medical Imaging Conference Record. 2012. s2576-2579.
- Choi WH, Um YH, Jung WS, Kim SH. Automated quantification of amyloid positron emission tomography: a comparison of PMOD and MIMNEURO. Annals of Nuclear medicine. 2016. 30: 682-689. https://doi.org/10.1007/s12149-016-1115-6
- DeLong ER, DeLong DM, Clarke-Pearson KL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988. 44: 837-845. https://doi.org/10.2307/2531595
- Goncalves AB, Souza JS, da Silva GG, Cereda MP, Pott A, Naka MH, Pistori H. Feature extraction and machine learning for the classification of Brazilian savannah pollen grains. PloS One. 2016. 11: e0157044. https://doi.org/10.1371/journal.pone.0157044
- Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Kim R. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama. 2016. 316: 2402-2410. https://doi.org/10.1001/jama.2016.17216
- Gunasekaran TI, Ohn T. MicroRNAs as Novel Biomarkers for the Diagnosis of Alzheimer's Disease and Modern Advancements in the Treatment. Biomedical Science Letters. 2015. 21: 1-8. https://doi.org/10.15616/BSL.2015.21.1.1
-
Haass C, Selkoe DJ. Soluble protein oligomers in neurodegeneration: lessons from the Alzheimer's amyloid
${\beta}$ -peptide. Nature Reviews Molecular Cell Biology. 2007. 8: 101. https://doi.org/10.1038/nrm2101 -
Illan IA, Gorriz JM, Ramirez J, Salas-Gonzalez D, Lopez MM, Segovia F, Chaves R, Gomez-Rio M, Puntonet CG. The Alzheimer's Disease Neuroimaging Initiative.
$^{18}F$ -FDG PET imaging analysis for computer aided Alzheimer's Diagnosis. 2011. 181: 903-916. https://doi.org/10.1016/j.ins.2010.10.027 - Kang H, Kim WG, Yang GS, Kim HW, Jeong JE, Yoon HJ, Cho K, Jeong YJ, Kang DY. VGG-based BAPL score classification of 18F-Florbetaben Amyloid Brain PET. J Exp Biomed Sci. 2018.
- Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017. 284: 574-582. https://doi.org/10.1148/radiol.2017162326
- Lopresti BJ, Klunk WE, Mathis CA, Hoge JA, Ziolko SK, Lu X, Price JC. Simplified quantification of Pittsburgh Compound B amyloid imaging PET studies: a comparative analysis. Journal of Nuclear Medicine. 2005. 46: 1959-1972.
- Lundeen TF, Seibyl JP, Covington MF, Eshghi N, Kuo PH. Signs and artifacts in Amyloid PET. Radio Graphics. 2018. 38: 2123-2133.
- Mockus J, Tiesis V, Zilinskas A. The Application of Bayesian Methods for Seeking the Extremum: Toward global optimization 2. 1978. pp 117. Elsevier. Amsterdam, Netherlands.
- Oh IS. Pattern recognition. 2008. pp 137-173. Kyobobook. Seoul, Korea.
- Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D. Scikit-learn: machine learning in python. Journal of Machine Learning Research. 12: 2825-2830.
- Piramal Imaging Limited. Neuraceq. Summary of product characteristics. Cambridge: Piramal Imaging Limited. 2014.
- Platt J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers. 1999. 10: 61-74.
-
Segovia F, Sanchez-Vano R, Gorriz JM, Ramirez J, Sopena-Novales P, Dardel NT, Gomez-Rio M. Using CT data to improve the quantitative analysis of
$^{18}F$ -FBB PET neuroimages. Frontiers in Aging Neuroscience. 2018. 10: 158. https://doi.org/10.3389/fnagi.2018.00158 - Seibyl J, Catafau AM, BARthel H, Ishii K, Rowe CC, Leverenz JB, Ghetti B, Ironside JW, Takao M, Akatsu H, Murayama S, Bullich S, Mueller A, Koglin N, Schulz-Schaeffer WJ, Hoffmann A, Sabbagh MN, Stephens AW, Sabri O. Impact of training method on the robustness of the visual assessment of 18F-Florbetaben PET scan: results from a phase-3 study. J Nucl Med. 2016. 57: 900-906. https://doi.org/10.2967/jnumed.115.161927
- Sherman M, Cessie SL. A comparison between bootstrap methods and generalized estimating equations for correlate outcomes in generalized linear models. Communications in Statistics-Simulation and Computation. 1997. 26: 901-925. https://doi.org/10.1080/03610919708813417
- Snoek J, Larochelle H, Adams RP. Pracical Bayesian optimization of machine learning algorithm. In Advances in Neural Information Processing System. 2012. 2951-2959.
- Taylor JC, Fenner JW. Comparison of machine learning and semiquantification algorithms for (I123) FP-CIT classification: the beginning of the end for semi-quantification?. EJNMMI Physics. 2017. 4: 29. https://doi.org/10.1186/s40658-017-0196-1
- Vapnik VN. 10.5 Support Vector Machine: Statistical Learning Theory. 1998. pp 421-441. Wiley-Interscience. Hoboken, USA.
- Varma S, Simon R. Bias in error estimation when using crossvalidation for model selection. BMC Bioinformatics. 2006. 7: 91. https://doi.org/10.1186/1471-2105-7-91
- Xue DX, Zhang R, Feng H, Wang YL. CNN-SVM for microvascular morphological type recognition with data augmentation. Journal of Medical and Biological Engineering. 2016. 36: 755-764. https://doi.org/10.1007/s40846-016-0182-4
- Zhang Y, Dong Z, Wu L, Wang S. A hybrid method for MRI brain image classification. Expert Systems with Applications. 2011. 38: 10049-10053. https://doi.org/10.1016/j.eswa.2011.02.012