Zhejiang Normal University - China
Professional Profile
SCOPUS
Early Academic Pursuits
Amina Benabid embarked on her academic journey with a Bachelor's degree in Mathematics from Université Frères Mentouri - Constantine 1, Algeria, which she completed in July 2016. She continued her education with a Master's degree in Mathematics from the same university, focusing on Statistical Tests, and graduated in July 2018. Her academic pursuits culminated in a Doctor of Natural Science in Mathematics from Zhejiang Normal University, China, in June 2022. Her doctoral thesis, supervised by Prof. Dao-Hong Xiang, explored the Convergence Theory of Large Margin Learning.
Professional Endeavors
Dr. Amina Benabid has gained diverse professional experience in both research and academic roles. She served as a Research Assistant at the School of Data Science, City University of Hong Kong, under the supervision of Prof. Ding-Xuan Zhou in July 2019. Subsequently, she held a Medical image analysis Postdoctoral fellowship position at Zhejiang Normal University's College of Mathematical Medicine from August 2022 to August 2024, working under the guidance of Prof. Yuan Jing.
Contributions and Research Focus
Dr. Benabid's research interests are focused on Statistical Learning Theory, Medical Image Analysis, Deep Learning, and Semantic Segmentation. She has made significant contributions to these fields through several publications and ongoing research projects. Her work includes papers Medical image analysis such as "Comparison theorems on large-margin learning" and "CFNet: Cross-scale Fusion Network for Brain Tumor Segmentation on 3D MRI Scans." Her research emphasizes integrating contextual information for enhanced brain tumor classification and developing real-time semantic segmentation models.
Accolades and Recognition
Throughout her career, Dr. Benabid has been recognized for her academic achievements and contributions. She received the Outstanding Award for academic innovation in 2021 and was honored as an Excellent Student in 2022. Her work has been acknowledged at various international conferences, including the 4th International Symposium on Artificial Intelligence for Medical Sciences and the 6th International Symposium on Medical image analysis Image Computing and Digital Medicine.
Impact and Influence
Dr. Amina Benabid's research has had a profound impact on advancing the understanding and application of deep learning techniques in medical image analysis, particularly in the context of brain tumor segmentation. Her contributions to the development of real-time semantic segmentation models using innovative approaches like CFNet and P2AT have set benchmarks in the field.
Legacy and Future Contributions
Looking ahead, Dr. Benabid aims to continue her research endeavors, focusing on further advancements in medical image analysis and deep learning. Her commitment to leveraging artificial intelligence for improving healthcare outcomes underscores her dedication to making significant contributions to the field. Through ongoing research and mentorship, she seeks to inspire future generations of researchers and contribute to transformative innovations in medical imaging technologies.
NOTABLE PUBLICATIONS