Early diagnosis and staging of prostate cancer using magnetic resonance imaging: State of the art and perspectives. (Alkadi, R., Taher, F., El-Baz, A. and Werghi, N., 2018. ) Prostate cancer imaging: An engineering and clinical perspective. [pdf] [demo]
A deep learning-based approach for the detection and localization of prostate cancer in T2 magnetic resonance images. (Alkadi, R., Taher, F., El-Baz, A. and Werghi, N., 2019.) Journal of digital imaging, 32, pp.793-807. [pdf] [demo]
A 2.5 D deep learning-based approach for prostate cancer detection on T2-weighted magnetic resonance imaging. (Alkadi, R., El-Baz, A., Taher, F. and Werghi, N., 2018.) In Proceedings of the European Conference on Computer Vision (ECCV) Workshops (pp. 0-0). [pdf] [demo]
Segmentation of abdominal aortic aneurysm (aaa) based on topology prior model. In Medical Image Understanding and Analysis: 21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11–13, 2017, Proceedings 21 (pp. 219-228). (Salahat, S., Soliman, A., McGloughlin, T., Werghi, N. and El-Baz, A., 2017.) Springer International Publishing. [pdf] [demo]
A comprehensive non-invasive framework for diagnosing prostate cancer. (Reda, I., Shalaby, A., Elmogy, M., Abou Elfotouh, A., Khalifa, F., Abou El-Ghar, M., Hosseini-Asl, E., Gimel’farb, G., Werghi, N. and El-Baz, A., 2017.) Computers in biology and medicine, 81, pp.148-158. [pdf] [demo]
Computer-aided diagnostic tool for early detection of prostate cancer. (Reda, I., Shalaby, A., Khalifa, F., Elmogy, M., Aboulfotouh, A., Abou El-Ghar, M., Hosseini-Asl, E., Werghi, N., Keynton, R. and El-Baz, A., 2016, September.) In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 2668-2672). IEEE. [pdf] [demo]
