Dr. Banafshe Felfeliyan - Health Professions - Best Researcher Award

University of Alberta - Canada

Professional Profiles

• Scopus

Early Academic Pursuits

Banafshe Felfeliyan's academic journey began with a strong interest in computer engineering, where she obtained her Bachelor's degree from Isfahan University of Technology, Iran. Subsequently, she pursued a Master's degree focusing on coronary vessel segmentation in X-Ray angiogram images. This laid the foundation for her doctoral studies in Biomedical Engineering at the University of Calgary, Canada, where she focused on the automatic quantification of osteoarthritis features in MRI using deep learning under the guidance of Dr. Janet Ronsky.

Professional Endeavors

Dr. Felfeliyan's professional career is marked by her commitment to advancing medical imaging through cutting-edge technology. As a Postdoctoral Research Fellow at the Radiology & Diagnostic Imaging Department, University of Alberta, Canada, she led the development and optimization of deep learning models for automated AI MRI biomarker profiles for osteoarthritis. Prior to this, she served as a Computer Research Engineer at the McCaig Institute, University of Calgary, where she focused on bone segmentation in MRI images using deep learning. Additionally, she contributed to retinal layer segmentation in retina OCT scans as an Associated Medical-Image Analysis Researcher at Aurteen Inc., Canada.

Contributions and Research Focus On Health Profession

Dr. Felfeliyan's research focus lies at the intersection of medical imaging, machine learning, and deep learning. Her doctoral thesis on automatic quantification of osteoarthritis features in MRI using deep learning represents a significant contribution to the field of biomedical engineering. Throughout her career, she has developed and optimized deep learning models for different modalities, representation learning, and vision-language processing in medical imaging. Her research endeavors have resulted in automatic, objective, and fast medical image assessment techniques, paving the way for advancements in diagnosis and treatment. Through their expertise and care, they contribute to improving the well-being and quality of life of individuals and communities.

Accolades and Recognition

Dr. Felfeliyan's exemplary contributions to biomedical imaging have been recognized through various accolades and awards. Her doctoral research earned her acclaim for its innovative approach to quantifying osteoarthritis features in MRI, garnering attention from peers and industry experts alike. Additionally, her contributions as a Postdoctoral Research Fellow at the University of Alberta have been instrumental in advancing the field of automated AI MRI biomarker profiling for osteoarthritis, earning her recognition as a leading researcher in the domain. Health professions encompass a diverse range of careers dedicated to promoting, maintaining, and restoring health.

Impact and Influence

Dr. Felfeliyan's research has had a profound impact on the field of biomedical imaging, particularly in the development of automated AI biomarker extraction techniques. By leveraging machine learning and deep learning methodologies, she has revolutionized medical image analysis, enabling clinicians to make more accurate and timely diagnoses. Her collaborative efforts with researchers and clinicians have facilitated the translation of her research findings into clinical practice, ultimately improving patient outcomes and advancing healthcare. Professionals in this field include doctors, nurses, therapists, and technicians, among others.

Legacy and Future Contributions In Health Profession

As Dr. Felfeliyan continues to pursue groundbreaking research in biomedical imaging, her legacy as a pioneering researcher is assured. Her innovative contributions to automated AI biomarker extraction and medical image analysis are poised to shape the future of healthcare, facilitating early disease detection and personalized treatment strategies. Through mentorship and collaboration, she aims to inspire the next generation of researchers to push the boundaries of knowledge and make meaningful contributions to the field of biomedical engineering. Dr. Felfeliyan's future contributions hold the promise of further advancing our understanding of disease pathology and enhancing patient care through cutting-edge technology.

Notable Publications

Improved-Mask R-CNN: Towards an accurate generic MSK MRI instance segmentation platform (data from the Osteoarthritis Initiative) 2022

Liver Segmentation in Abdominal CT Images Using Probabilistic Atlas and Adaptive 3D Region Growing 2019