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Curriculum Learning for Age Estimation from Brain MRI


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[1] L. K. Afshar and H. Sajedi, “Age prediction based on brain MRI images using extreme learning machine,” in 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), Bojnord, Iran, Apr. 2019, pp. 1–5. https://doi.org/10.1109/CFIS.2019.869215610.1109/CFIS.2019.8692156 Search in Google Scholar

[2] H. Sajedi and N. Pardakhti, “Age prediction based on brain MRI image: A survey,” Journal of Medical Systems, vol. 43, no. 8, Art no. 279, Jul. 2019. https://doi.org/10.1007/s10916-019-1401-710.1007/s10916-019-1401-731297614 Search in Google Scholar

[3] B. Wang, T. D. Pham, “MRI-based age prediction using hidden Markov models,” Journal of Neuroscience Methods, vol. 199, no. 1, pp. 140–145, Jul. 2011. https://doi.org/10.1016/j.jneumeth.2011.04.02210.1016/j.jneumeth.2011.04.02221549147 Search in Google Scholar

[4] J. De Tobel, E. Hillewig, M. B. de Haas, B. Van Eeckhout, S. Fieuws, P. W. Thevissen, and K. L. Verstraete, “Forensic age estimation based on T1 SE and VIBE wrist MRI: Do a one-fits-all staging technique and age estimation model apply?” European radiology, vol. 29, no. 6, pp. 2924–2935, Jan. 2019. https://doi.org/10.1007/s00330-018-5944-710.1007/s00330-018-5944-730617494 Search in Google Scholar

[5] D. Štern, C. Payer, N. Giuliani, and M. Urschler, “Automatic age estimation and majority age classification from multi-factorial MRI data,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 4, pp. 1392–1403, Sep. 2018. https://doi.org/10.1109/JBHI.2018.286960610.1109/JBHI.2018.286960631059459 Search in Google Scholar

[6] S. Tangmose, K. E. Jensen, C. Villa, and N. Lynnerup, “Forensic age estimation from the clavicle using 1.0 T MRI – preliminary results”, Forensic Science International, vol. 234, pp. 7–12, Jan. 2014. https://doi.org/10.1016/j.forsciint.2013.10.02710.1016/j.forsciint.2013.10.02724378295 Search in Google Scholar

[7] J. A. Krämer, S. Schmidt, K.-U. Juärgens, M. Lentschig, A. Schmeling, and V. Vieth, “Forensic age estimation in living individuals using 3.0 T MRI of the distal femur,” International Journal of Legal Medicine, vol. 128, no. 3, pp. 509–514, Feb. 2014. https://doi.org/10.1007/s00414-014-0967-310.1007/s00414-014-0967-324504560 Search in Google Scholar

[8] B. Neumayer, M. Schloegl, C. Payer, T. Widek, S. Tschauner, T. Ehammer, R. Stollberger, and M. Urschler, “Reducing acquisition time for MRI- based forensic age estimation,” Scientific Reports, vol. 8, Art no. 2063, pp. 1–9, 2018. https://doi.org/10.1038/s41598-018-20475-110.1038/s41598-018-20475-1579491929391552 Search in Google Scholar

[9] D. Štern, C. Payer, V. Lepetit, M. Urschler, “Automated age estimation from hand MRI volumes using deep learning,” in International conference on medical image computing and computer-assisted intervention, Lecture Notes in Computer Science, vol 9901, Springer, 2016, pp. 194–202. https://doi.org/10.1007/978-3-319-46723-8_2310.1007/978-3-319-46723-8_23 Search in Google Scholar

[10] F. Fan, K. Zhang, Z. Peng, J.-H. Cui, N. Hu, Z.-H. Deng, “Forensic age estimation of living persons from the knee: comparison of MRI with radiographs,” Forensic Science International, vol. 268, pp. 145–150, Nov. 2016. https://doi.org/10.1016/j.forsciint.2016.10.00210.1016/j.forsciint.2016.10.00227770721 Search in Google Scholar

[11] B. Neumayer et al., “The four-minute approach revisited: accelerating MRI-based multi-factorial age estimation,” International Journal of Legal Medicine, vol. 134, pp. 1475–1485, Dec. 2019. https://doi.org/10.1007/s00414-019-02231-w10.1007/s00414-019-02231-w Search in Google Scholar

[12] E. Hillewig et al., “Magnetic resonance imaging of the sternal extremity of the clavicle in forensic age estimation: towards more sound age estimates,” International Journal of Legal Medicine, vol. 127, no. 3, pp. 677–689, Dec. 2013. https://doi.org/10.1007/s00414-012-0798-z10.1007/s00414-012-0798-z Search in Google Scholar

[13] Y. Guo et al., “Dental age estimation in living individuals using 3.0 T MRI of lower third molars,” International Journal of Legal Medicine, vol. 129, no. 6, pp. 1265–1270, Aug. 2015. https://doi.org/10.1007/s00414-015-1238-710.1007/s00414-015-1238-7 Search in Google Scholar

[14] P. Tramini, B. Bonnet, R. Sabatier, and L. Maury, “A method of age estimation using raman microspectrometry imaging of the human dentin,” Forensic Science International, vol. 118, no. 1, pp. 1–9, Apr. 2001. https://doi.org/10.1016/S0379-0738(00)00352-210.1016/S0379-0738(00)00352-2 Search in Google Scholar

[15] D. J. Madden, W. L. Whiting, S. A. Huettel, L. E. White, J. R. MacFall, and J. M. Provenzale, “Diffusion tensor imaging of adult age differences in cerebral white matter: Relation to response time,” NeuroImage, vol. 21, no. 3, pp. 1174–1181, Mar. 2004. https://doi.org/10.1016/j.neuroimage.2003.11.00410.1016/j.neuroimage.2003.11.00415006684 Search in Google Scholar

[16] D. P. Varikuti et al., “Evaluation of non-negative matrix factorization of grey matter in age prediction,” NeuroImage, vol. 173, pp. 394–410, June 2018. https://doi.org/10.1016/j.neuroimage.2018.03.00710.1016/j.neuroimage.2018.03.007591119629518572 Search in Google Scholar

[17] L. Su, L. Wang, and D. Hu, “Predicting the age of healthy adults from structural MRI by sparse representation,” in International Conference on Intelligent Science and Intelligent Data Engineering, Lecture Notes in Computer Science, vol 7751, Springer, 2012, pp. 271–279. https://doi.org/10.1007/978-3-642-36669-7_3410.1007/978-3-642-36669-7_34 Search in Google Scholar

[18] P. Lam, A. H. Zhu, I. B. Gari, N. Jahanshad, and P. M. Thompson, “3D grid-attention networks for interpretable age and Alzheimer’s disease prediction from structural MRI,” arXiv, Art no. 2011.09115, 2020. Search in Google Scholar

[19] I. Beheshti, S. Mishra, D. Sone, P. Khanna, and H. Matsuda, “T1-weighted MRI-driven brain age estimation in Alzheimer’s disease and Parkinson’s disease,” Aging and Disease, vol. 11, no. 3, pp. 618–628, 2020. https://doi.org/10.14336/AD.2019.061710.14336/AD.2019.0617722028132489706 Search in Google Scholar

[20] M. Ueda, K. Ito, K. Wu, K. Sato, Y. Taki, H. Fukuda, and T. Aoki, “An age estimation method using 3D-CNN from brain MRI images,” in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, Apr. 2019, pp. 380–383. https://doi.org/10.1109/ISBI.2019.875939210.1109/ISBI.2019.8759392 Search in Google Scholar

[21] T.-W. Huang et al., “Age estimation from brain MRI images using deep learning,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, Australia, Apr. 2017, pp. 849–852. https://doi.org/10.1109/ISBI.2017.795065010.1109/ISBI.2017.7950650 Search in Google Scholar

[22] K. Armanious et al., “Age-net: An MRI-based iterative framework for brain biological age estimation,” IEEE Transactions on Medical Imaging, vol. 40, no. 7, pp. 1778–1791, July 2021. https://doi.org/10.1109/TMI.2021.306685710.1109/TMI.2021.306685733729932 Search in Google Scholar

[23] K. Franke et al., “Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters,” NeuroImage, vol. 50, no. 3, pp. 883–892, Apr. 2010. https://doi.org/10.1016/j.neuroimage.2010.01.00510.1016/j.neuroimage.2010.01.00520070949 Search in Google Scholar

[24] C.-L. Chen et al., “Generalization of diffusion magnetic resonance imaging–based brain age prediction model through transfer learning,” NeuroImage, vol. 217, Art no. 116831, Aug. 2020. https://doi.org/10.1016/j.neuroimage.2020.11683110.1016/j.neuroimage.2020.11683132438048 Search in Google Scholar

[25] L. Lin, G. Zhang, J. Wang, M. Tian, and S. Wu, “Utilizing transfer learning of pre-trained AlexNet and relevance vector machine for regression for predicting healthy older adult’s brain age from structural MRI,” Multimedia Tools and Applications, vol. 80, pp. 24719–24735, Apr. 2021. https://doi.org/10.1007/s11042-020-10377-810.1007/s11042-020-10377-8 Search in Google Scholar

[26] Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in Proceedings of the 26th Annual International Conference on Machine Learning, June 2009, pp. 41–48. https://doi.org/10.1145/1553374.155338010.1145/1553374.1553380 Search in Google Scholar

[27] W. Wang, T. Ishikawa, and H. Watanabe, “Facial age estimation by curriculum learning,” in 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), Kobe, Japan, June 2020, pp. 138–139. https://doi.org/10.1109/GCCE50665.2020.929192910.1109/GCCE50665.2020.9291929 Search in Google Scholar

[28] K. Li, J. Xing, W. Hu, and S. J. Maybank, “D2c: Deep cumulatively and comparatively learning for human age estimation,” Pattern Recognition, vol. 66, pp. 95–105, June 2017. https://doi.org/10.1016/j.patcog.2017.01.00710.1016/j.patcog.2017.01.007 Search in Google Scholar

[29] J. Kim, W. Bae, K.-H. Jung, I.-S. Song, Development and validation of deep learning-based algorithms for the estimation of chronological age using panoramic dental x-ray images (2019). Search in Google Scholar

[30] Gazi Brains 2020 Datasetdoi:10.7303/syn22159468. Search in Google Scholar

[31] S. Ji, W. Xu, M. Yang, K. Yu, 3d convolutional neural networks for human action recognition, IEEE transactions on pattern analysis and machine intelligence 35 (1) (2012) 221–231.10.1109/TPAMI.2012.5922392705 Search in Google Scholar

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