o (2011.06) Bachelor's Degree, Cairo University, Egypt.
o (2015.02) M.Sc., Cairo University, Egypt.
o (2019.08) Ph.D., Kyung Hee University, Republic of Korea.

o (2012.01~2012.09) Biomedical Maintenance Engineer, Modern Medical Tech. Est., Yemen.
o (2015.03~2015.08) Lecturer, Technical Radiology Department, Science & Technology University, Yemen.
o (2015.03~2015.08) Lecturer, Department of Biomedical Engineering, Yemeni Jordanian University, Yemen.
o (2015.09~2019.08) Research Assistant, Bio-imaging Laboratory, Kyung Hee University, Republic of Korea.
o (2015.10~2019.08) Software Developer, Medical Engineering R&D Center, YOZMA BMTech Co., Ltd, Seoul, Republic of Korea.
o (2019.09~2020.10) Postdoctoral Researcher, Yonsei University, Seoul, Republic of Korea.
o (2020.11~2022.08) Research Professor, Yonsei University, Seoul, Republic of Korea.
o (2022.09~Present) Assistant Professor, Department of Artificial Intelligence, Sejong University, Seoul, Republic of Korea.

1. Al-masni M. A. et al., "A Knowledge Interaction Learning for Multi-Echo MRI Motion Artifact Correction towards Better Enhancement of SWI," Computers in Biology and Medicine, vol. 153, pp. 106553, 2023.

2. Haejoon Lee, ..., Al-masni M. A., "Detection of Cerebral Microbleeds in MR Images using a Single-Stage Triplanar Ensemble Detection Network (TPE-Det)," Journal of Magnetic Resonance Imaging, 2022.

3. Al-masni M. A. et al., “Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRI”, NeuroImage, vol. 259, pp. 119411, 2022.

4. Al-masni M. A. et al., “CMM-Net: Contextual Multi-Scale Multi-Level Network for Efficient Biomedical Image Segmentation,” Scientific Reports, 10191 , 2021.

5. Al-masni M. A. et al., “3D Multi-Scale Residual Network Toward Lacunar Infarcts Identification from MR Images with Minimal User Intervention,” IEEE Access, vol. 9, pp. 11787-11797, 2021.

6. Al-masni M. A. et al., “Automated Detection of Cerebral Microbleeds in MR Images: A Two-Stage Deep Learning Approach,” NeuroImage: Clinical, vol. 28, pp. 102464, 2020.

7. Chung H., ..., Al-masni M. A., “Stenosis Detection From Time-of-Flight Magnetic Resonance Angiography via Deep Learning 3D Squeeze and Excitation Residual Networks,” IEEE Access, vol. 8, pp. 43325 - 43335, 2020.

8. Al-masni M. A. et al., “Multiple Skin Lesions Diagnostics via Integrated Deep Convolutional Networks for Segmentation and Classification,” Computer Methods and Programs in Biomedicine, vol. 190, pp. 105351, 2020.

9. Al-masni M. A. et al., “Skin Lesion Segmentation in Dermoscopy Images via Deep Full Resolution Convolutional Networks,” Computer Methods and Programs in Biomedicine, vol. 162, pp. 221-231, 2018.

10.  Al-antari M. A., ..., Al-masni M. A., “A fully Integrated Computer-Aided Diagnosis System for Digital X-ray Mammograms via Deep Learning Detection, Segmentation, and Classification,” International Journal of Medical Informatics, vol. 117, pp. 44-54, 2018.

11. Al-masni M. A. et al., “Simultaneous Detection and Classification of Breast Masses in Digital Mammograms via a Deep Learning YOLO-based CAD System,” Computer Methods and Programs in Biomedicine, vol. 157C, pp. 85-94, 2018.