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.
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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
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DOI: https://doi.org/10.1007/978-3-030-18305-9_51
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