Abstract
With the release of large-scale bone age assessment datasets and competitions looking at solving the problem of bone age estimation, there has been a large boom of machine learning in medical imaging which has attempted to solve this problem. Although many of these approaches use convolutional neural networks, they often include some specialized form of preprocessing which is often lengthy. We propose using a subpixel convolution layer in addition to an attention mechanism similar to those developed by Luong et al. in order to overcome some of the implicit problems with assuming particular placement and orientation of radiographs due to forced preprocessing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gilsanz, V., Ratib, O.: Hand Bone Age A Digital Atlas of Skeletal Maturity, 1st edn. Springer, Los Angeles (2005). https://doi.org/10.1007/978-3-642-23762-1
Halabi, S.S., Prevedello, L.M., Kalpathy-cramer, J., Mamonov, A.B.: The RSNA pediatric bone age machine learning challenge, pp. 1–6 (2018). https://doi.org/10.1148/radiol.2018180736
Iglovikov, V.I., Rakhlin, A., Kalinin, A.A., Shvets, A.A.: Paediatric bone age assessment using deep convolutional neural networks. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 300–308. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_34
Kim, J.R., et al.: Computerized bone age estimation using deep learning-based program: Evaluation of the accuracy and efficiency. Am. J. Roentgenol. 209(6), 1374–1380 (2017). https://doi.org/10.2214/AJR.17.18224
Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1412–1421 (2015). https://doi.org/10.18653/v1/D15-1166, https://re-work.co/blog/deep-learning-ilya-sutskever-google-openai
Mader, K.: Kaggle RSNA Bone Age Dataset (2018). https://www.kaggle.com/kmader/rsna-bone-age
Manzoor Mughal, A., Hassan, N., Ahmed, A.: Bone age assessment methods: a critical review 30(1), 211–215 (2014). https://doi.org/10.12669/pjms.301.4295
Mutasa, S., Chang, P.D., Ruzal-Shapiro, C., Ayyala, R.: MABAL: a novel deep-learning architecture for machine-assisted bone age labeling. J. Digit. Imaging 1–7 (2018). https://doi.org/10.1007/s10278-018-0053-3
Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural. Network (2016). https://doi.org/10.1109/CVPR.2016.207
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Gasmallah, M., Zulkernine, F., Rivest, F., Mousavi, P., Sedghi, A. (2019). Fully End-To-End Super-Resolved Bone Age Estimation. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_51
Download citation
DOI: https://doi.org/10.1007/978-3-030-18305-9_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18304-2
Online ISBN: 978-3-030-18305-9
eBook Packages: Computer ScienceComputer Science (R0)