Adler, J., & Parmryd, I. (2010). Quantifying colocalization by correlation: the pearson correlation coefficient is superior to the mander’s overlap coefficient. Cytometry Part A., 77(8), 733–742. https://doi.org/10.1002/cyto.a.20896

Article  Google Scholar 

Akinci D’Antonoli, T., Todea, R.-A., Leu, N., Datta, A. N., Stieltjes, B., Pruefer, F., & Wasserthal, J. (2023). Development and evaluation of deep learning models for automated estimation of myelin maturation using pediatric brain mri scans. Radiology Artificial Intelligence.,5(5), 220292.

Babaji, I., & Kumar, S. (2021). Mri-based diagnosis of leukodystrophy: A systematic review. Brain and Development.,43(6), 518–530. https://doi.org/10.1016/j.braindev.2021.04.003

Carmody, D. P., Dunn, S. M., Boddie-Willis, A. S., DeMarco, J. K., & Lewis, M. (2004). A quantitative measure of myelination development in infants, using mr images. Neuroradiology, 46, 781–786. https://doi.org/10.1007/s00234-004-1241-z

Article  PubMed  PubMed Central  Google Scholar 

Chang, N., & Zhang, D. (2021). Artificial intelligence in myelin imaging: Using deep learning to detect abnormal myelination in pediatric mri. Journal of Neural Engineering,18(5), 050501. https://doi.org/10.1088/1741-2552/abf218

Chen, J. V., Chaudhari, G., Hess, C. P., Glenn, O. A., Sugrue, L. P., Rauschecker, A. M., & Li, Y. (2022). Deep learning to predict neonatal and infant brain age from myelination on brain mri scans. Radiology, 305(3), 678–687. https://doi.org/10.1148/radiol.211860

Article  PubMed  Google Scholar 

Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. https://doi.org/10.48550/arXiv.1610.02357.

Deng, J., Dong, W., Socher, R., Li, L.-J., & Li, F.-F. (2009). Imagenet: A large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition, 248–255. https://doi.org/10.1109/CVPR.2009.5206848

Devlin, J. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. https://doi.org/10.48550/arXiv.1810.04805,arXiv:1810.04805

Dipnall, L. M., Yang, J. Y. M., Chen, J., Fuelscher, I., Craig, J. M., & Silk, T. J. (2024). Childhood development of brain white matter myelin: a longitudinal t1w/t2w-ratio study. Brain Structure and Function, 229(1), 151–159. https://doi.org/10.1007/s00429-023-02718-8

Article  CAS  PubMed  Google Scholar 

Dosovitskiy, A. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. https://doi.org/10.48550/arXiv.2010.11929,arXiv:2010.11929

Du, F., Yu, F., & Wang, X. (2019). Tracking brain development with mri: Age-related changes in myelination and brain maturation. Brain Development., 41(2), 139–147. https://doi.org/10.1016/j.braindev.2018.12.003

Article  Google Scholar 

Giavarina, D. (2015). Understanding bland altman analysis. Biochemia Medica, 25(2), 141–151. https://doi.org/10.11613/BM.2015.015

Article  PubMed  PubMed Central  Google Scholar 

Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022). Attention mechanisms in computer vision: A survey. Computational Visual Media, 8(3), 331–368. https://doi.org/10.1007/s41095-022-0266-8

Article  Google Scholar 

Hagiwara, A., Suzuki, M., Hori, M., & Aoki, S. (2023). Decoding brain development and aging: Pioneering insights from mri techniques. Investigative Radiology, 58(12), 841–851. https://doi.org/10.1097/RLI.0000000000000955

Article  CAS  Google Scholar 

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778. https://doi.org/10.1109/CVPR.2016.90

Hintze, J. L., & Nelson, R. D. (1998). Violin plots: A box plot-density trace synergism. The American Statistician, 52(2), 181–189. https://doi.org/10.2307/2685478

Article  Google Scholar 

Hong, J., Feng, Z., Wang, S., Peet, A., Zhang, Y., Sun, Y., & Yang, M. (2020). Brain age prediction of children using routine brain MR images via deep learning. Front Neurol,11, 584682. PUBMED. https://doi.org/10.3389/fneur.2020.584682.

Huang, G., Liu, Z., Maaten, L., & Weinberger, K.Q. (2016). Densely connected convolutional networks. https://doi.org/10.48550/arXiv.1608.06993,arXiv:1608.06993

Huang, G., Liu, Z., Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708. https://doi.org/10.1109/CVPR.2017.243

Lawrence, I., & Lin, K. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics, 255–268. https://doi.org/10.2307/2532051

Li, X., Hao, Z., Li, D., Jin, Q., Tang, Z., Yao, X., & Wu, T. (2024). Brain age prediction via cross-stratified ensemble learning. NeuroImage, 299, Article 120825. https://doi.org/10.1016/j.neuroimage.2024.120825

Article  PubMed  Google Scholar 

Mathivanan, S. K., Sonaimuthu, S., Murugesan, S., Rajadurai, H., Shivahare, B. D., & Shah, M. A. (2024). Employing deep learning and transfer learning for accurate brain tumor detection. Scientific Reports, 14(1), 7232. https://doi.org/10.1038/s41598-024-05732-8

Article  CAS  PubMed  PubMed Central  Google Scholar 

Newton, A., & Carter, H. (2020). Neuroimaging findings in pelizaeus-merzbacher disease: A case study. Neuroimaging Clinics of North America, 30(2), 201–210. https://doi.org/10.1016/j.nic.2020.01.008

Article  Google Scholar 

Prakash, R. M., Kumari, R. S. S., Valarmathi, K., & Ramalakshmi, K. (2023). Classification of brain tumours from mr images with an enhanced deep learning approach using densely connected convolutional network. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11(2), 266–277. https://doi.org/10.1080/21681163.2021.1993087

Article  Google Scholar 

Ramachandran, P., et al. (2017). Search space partitioning for neural architecture search. ICML. https://doi.org/10.48550/arXiv.1806.09055.

Sandler, M., Howard, A. G., Zhu, M., Zhmoginov, A., & Matusik, W. (2018). Mobilenetv 2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4510–4520. https://doi.org/10.1109/CVPR.2018.00474

Simonyan, K., & Zisserman, A. (2014). Vgg19: A deep convolutional network for large-scale image recognition. https://doi.org/10.48550/arXiv.1409.1556.

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818–2826. https://doi.org/10.1109/CVPR.2016.308

Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. C. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI conference on artificial intelligence,31(1). https://doi.org/10.1609/aaai.v31i1.11231

Tatachar, A. V. (2021). Comparative assessment of regression models based on model evaluation metrics. International Journal of Innovative Technology and Exploring Engineering, 8(9), 853–860. https://doi.org/10.35940/ijitee.I7926.078920.

Article  Google Scholar 

Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, (pp. 6105–6114). https://doi.org/10.48550/arXiv.1905.11946. PMLR.

Tang, J., Yang, P., Xie, B., Wei, C., Hu, L., Wang, S., Jiang, Y., Li, R., Zhou, H., Xu, H., Wan, Q., Han, J., Zhao, D., & Lu, L. (2023). A deep learning-based brain age prediction model for preterm infants via neonatal MRI. IEEE Access, 11, 68994–69004. https://doi.org/10.1109/ACCESS.2023.3291810

Article  Google Scholar 

Terzi, D. S., & Azginoglu, N. (2023). In-domain transfer learning strategy for tumor detection on brain mri. Diagnostics, 13(12), 2110. https://doi.org/10.3390/diagnostics13122110

Article  PubMed  PubMed Central  Google Scholar 

Valverde, J. M., Imani, V., Abdollahzadeh, A., De Feo, R., Prakash, M., Ciszek, R., & Tohka, J. (2021). Transfer learning in magnetic resonance brain imaging: A systematic review. Journal of Imaging, 7(4), 66. https://doi.org/10.3390/jimaging7040066

Article  PubMed  PubMed Central  Google Scholar 

Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems https://doi.org/10.48550/arXiv.1706.03762.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In: Advances in Neural Information Processing Systems (NeurIPS), (vol. 30, pp. 5998–6008). Curran Associates Inc. https://doi.org/10.5555/3295222.3295349

Vuradin, M., & Bozek, J. (2023). Neural network for neonatal brain age prediction from structural mr images. In: 2023 65th International Symposium ELMAR (ELMAR), (pp. 83–86). IEEE. https://doi.org/10.1109/ELMAR59410.2023.10253937.

Wada, A., Saito, Y., Fujita, S., Irie, R., Akashi, T., Sano, K., Kato, S., Ikenouchi, Y., Hagiwara, A., Sato, K., Tomizawa, N., Hayakawa, Y., Kikuta, J., Kamagata, K., Suzuki, M., Hori, M., Nakanishi, A., & Aoki, S. (2023). Automation of a rule-based workflow to estimate age from brain mr imaging of infants and children up to 2 years old using stacked deep learning. Magnetic Resonance in Medical Sciences, 22(1), 57–66. https://doi.org/10.2463/mrms.mp.2021-0068

Article  PubMed  Google Scholar 

Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate Research, 30(1), 79–82. https://doi.org/10.3354/cr030079

Article  Google Scholar 

Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016). A survey of transfer learning. Journal of Big Data, 3, 1–40. https://doi.org/10.1186/s40537-016-0043-6

Article  Google Scholar 

Woo, S., Park, J., Lee, J.-Y., Yoon, S., Kim, H., & So, K.H. (2018). Cbam: Convolutional block attention module. European Conference on Computer Vision (ECCV), 3–19. https://doi.org/10.1007/978-3-030-01234-2_1

Yang, Z., Salakhutdinov, R., & Hinton, G. (2018). Cross-modal attention for image captioning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7529–7538. https://doi.org/10.1109/CVPR.2018.00787

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems,27. https://doi.org/10.5555/2969033.2969197

Zhu, H., Wang, L., Shen, N., Wu, Y., Feng, S., Xu, Y., Chen, C., & Chen, W. (2023). Ms-hnn: Multi-scale hierarchical neural network with squeeze and excitation block for neonatal sleep staging using a single-channel eeg. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 2195–2204. https://doi.org/10.1109/TNSRE.2023.3314140

Article  PubMed  Google Scholar