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Items where Author is "Al-Arif, S. M."

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Riaz, A., Asad, M., Al-Arif, S. M. , Alonso, E. ORCID: 0000-0002-3306-695X, Dima, D. ORCID: 0000-0002-2598-0952, Corr, P. J. ORCID: 0000-0002-7618-0058 & Slabaugh, G. G. (2018). DeepFMRI: And End-to-End Deep Network for Classification of FRMI Data. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1419-1422. doi: 10.1109/ISBI.2018.8363838

Slabaugh, G. G., Knapp, K. & Al-Arif, S. M. (2017). Probabilistic Spatial Regression using a Deep Fully Convolutional Neural Network. In: Proceedings of the British Machine Vision Conference (BMVC). (154.1-154.12). BMVA Press.

Al-Arif, S. M., Gundry, M., Slabaugh, G. G. & Knapp, K. (2017). Global Localization and Orientation of the Cervical Spine in X-ray Imaging. In: Computational Methods and Clinical Applications for Spine Imaging. CSI 2016. Computational Methods and Clinical Applications for Spine Imaging, 17 Oct 2016, Athens, Greece.

Al-Arif, S. M., Gundry, M., Knapp, K. & Slabaugh, G. G. (2017). Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae. In: Yao, J., Vrtovec, T., Zheng, G. , Frangi, A., Glocker, B. & Li, S. (Eds.), Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae. Lecture Notes in Computer Science, 10182. (pp. 3-15). Cham: Springer.

Al-Arif, S. M., Asad, M., Knapp, K. , Gundry, M. & Slabaugh, G. G. (2015). Hough Forest-based Corner Detection for Cervical Spine Radiographs. Paper presented at the Medical Image Understanding and Analysis Conference, MIUA 2015, 15-07-2015 - 17-07-2015, University of Lincoln, UK.

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