University of Alberta-Canada
Author Profile
Early Academic Pursuits
Dr. Banafshe Felfeliyan embarked on her academic journey with a Bachelor's degree in Computer Engineering from Isfahan University of Technology, Iran, spanning from September 2010 to August 2014. Her enthusiasm for the intersection of computer science and medical applications led her to pursue a Master's degree in Computer Engineering from the same institution, focusing on coronary vessel segmentation in X-Ray angiogram images. This laid the foundation for her expertise in medical imaging and computer vision.
Continuing her academic pursuit, Banafshe pursued a Ph.D. in Biomedical Engineering with a focus on Medical Imaging at the University of Calgary, Canada, from September 2018 to December 2022. Her doctoral research, conducted under the guidance of Dr. Janet Ronsky, delved into the development of DL models for the automatic quantification of osteoarthritis features in MRI. This marked a significant step in her academic journey, blending her background in computer engineering with a deep dive into the complexities of medical imaging and deep learning.
Professional Endeavors
Dr. Banafshe transitioned seamlessly into the professional realm, taking on the role of a Computer Research Engineer at the McCaig Institute, University of Calgary, from September 2017 to August 2018. During this tenure, she researched state-of-the-art deep learning models, applied them to bone segmentation in MRI images, and communicated her findings at conferences and to lay audiences.
Subsequently, Banafshe took up the role of a Postdoctoral Research Fellow at the Radiology & Diagnostic Imaging Department, University of Alberta, Canada, from May 2023 to the present. Here, she has been actively engaged in the Northern Institute for Deep Learning in Ultrasound (NIDUS) project, focusing on the automated AI MRI biomarker profile for osteoarthritis. Her responsibilities include developing and optimizing DL models, conducting research on vision-language processing, and collaborating with researchers and clinicians.
Contributions and Research Focus On Medical Imaging
Dr. Banafshe's contributions in the field of medical imaging and deep learning are evident from her numerous publications and presentations. Her research spans various areas, including the application of vision-language models for assessing osteoarthritis, development of self-supervised learning models for ultrasound regional segmentation, and the use of CNNs for rotator cuff tendon tear detection from shoulder ultrasound images. Her work extends to weakly supervised medical image segmentation, domain adaptation, and the application of deep learning in diverse medical scenarios.
Banafshe's commitment to advancing the field is reflected in her involvement in grant and proposal writing, mentoring students, and leading the Osteoarthritis (OA) sub-group for the clinical translation of developed models.
Accolades and Recognition
Dr. Banafshe's contributions have not gone unnoticed, as evidenced by the numerous honors and awards she has received. Notably, she was honored as one of the top 15 young female scientists in Canada at the SCWIST Symposium in 2021. Her excellence in research and innovation has been recognized through awards such as the Alberta Innovates Postdoctoral Recruitment Fellowship, the Biomedical Engineering Research Excellence Award, and the AI Week Talent Bursary from the Alberta Machine Intelligence Institute (AMII).
Impact and Influence
Dr. Banafshe's impact extends beyond her individual accomplishments. Her involvement in teaching and mentorship, both as a Teaching Assistant at the University of Calgary and as a mentor to undergraduate students, showcases her commitment to fostering the next generation of researchers and engineers. Additionally, her leadership roles in various academic and scientific societies highlight her influence in shaping the academic community.
Legacy and Future Contributions
Dr. Banafshe Felfeliyan has already left a lasting legacy through her research, publications, and mentorship. Her multidisciplinary approach, combining computer engineering with biomedical engineering, has paved the way for innovative solutions in medical imaging and artificial intelligence. Looking forward, her continued contributions are expected to further advance the fields of automated AI biomarkers extraction, medical imaging, and deep learning, leaving a lasting impact on the intersection of technology and healthcare.
Citations
- Citations 177
- h-index 6
- i10-index 5
Notable Publication