Have a personal or library account? Click to login
Curriculum Learning for Age Estimation from Brain MRI Cover

Curriculum Learning for Age Estimation from Brain MRI

Open Access
|Dec 2021

References

  1. [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
  2. [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
  3. [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
  4. [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
  5. [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
  6. [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
  7. [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
  8. [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
  9. [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
  10. [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
  11. [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
  12. [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
  13. [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
  14. [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
  15. [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
  16. [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
  17. [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
  18. [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.
  19. [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
  20. [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
  21. [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
  22. [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
  23. [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
  24. [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
  25. [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
  26. [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
  27. [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
  28. [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
  29. [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).
  30. [30] Gazi Brains 2020 Datasetdoi:10.7303/syn22159468.
  31. [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
DOI: https://doi.org/10.2478/acss-2021-0014 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683
Language: English
Page range: 116 - 121
Published on: Dec 30, 2021
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2021 Alican Asan, Ramazan Terzi, Nuh Azginoglu, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.