Noelle Clerkin | Health Professions | Best Researcher Award

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Ms. Noelle Clerkin | Health Professions | Best Researcher Award

University of Suffolk | United Kingdom

Author profile

Orcid

Early Academic Pursuits 🎓

Ms. Noelle Clerkin embarked on her academic journey with a foundation in health sciences. She completed her BSc (Hons) in Diagnostic Radiography from the University Campus Suffolk in 2009, following her outstanding performance in secondary education, where she earned several honors. Her academic progression is marked by continuous specialization, including a Postgraduate Certificate in Mammography from University College Dublin (2013), further deepening her expertise in breast imaging and clinical communication.

Professional Endeavors 👩‍⚕️

Ms. Clerkin's professional career is diverse and impactful. She currently serves as the Breast Service Manager at the Belfast & Social Health and Care Trust, where she leads the breast imaging services with a strong focus on service modernization and clinical excellence. Her previous roles include serving as a Deputy Breast Service Manager, Lead Clinical Practice Educator, and Advanced Practitioner at the Belfast Trust. She has also been actively involved in academia, contributing as a Visiting Lecturer at the University of Suffolk and an External Examiner at Kingston University.

Contributions and Research Focus 🔬

With a strong focus on breast cancer screening, Ms. Clerkin has made significant contributions through her research and publications. Her work explores factors influencing diagnostic performance in mammography reporting. She has authored papers on the role of Radiography Advanced Practitioners (RAPs) and their impact on breast cancer screening outcomes. Her research has been featured in leading journals like Radiography and presented at international conferences, such as the European Congress of Radiology and UK Imaging and Oncology Congress, highlighting her commitment to advancing breast cancer diagnostics.

Accolades and Recognition 🏆

Ms. Clerkin’s expertise in radiography has been recognized through her multiple speaking engagements and presentations at prestigious conferences across Europe. Her research has earned her a reputable presence in the field of breast cancer imaging, with several conference presentations on RAP performance in breast cancer screening. This continuous contribution to knowledge underscores her role as a thought leader in the radiography and oncology communities.

Impact and Influence 🌍

As both a healthcare leader and academic, Ms. Clerkin has greatly influenced the fields of breast imaging and clinical education. She has played a pivotal role in shaping breast screening services in the Belfast Trust and has contributed to the education and development of radiographers. Her partnership with educational institutions such as the Nottingham Breast Institute of Education further emphasizes her impact on the future of healthcare professionals in the radiography sector.

Legacy and Future Contributions 🌟

Ms. Noelle Clerkin’s legacy lies in her dual roles as a clinical leader and academic scholar. Her ongoing PhD research in Health and Biological Sciences promises to yield further insights into breast cancer diagnostics, while her leadership in breast screening services ensures she continues to make a direct impact on patient care. Looking ahead, her work will likely shape the evolution of breast imaging services and radiography education for years to come.

 

Publications

📄 An initial exploration of factors that may impact radiographer performance in reporting mammograms
Authors: N. Clerkin, C. Ski, M. Suleiman, Z. Gandomkar, P. Brennan, R. Strudwick
Journal: Radiography
Year: 2024

📄 Identification of factors associated with diagnostic performance variation in reporting of mammograms: a review
Authors: N. Clerkin, C.F. Ski, P.C. Brennan, R. Strudwick
Journal: Radiography
Year: 2023

📄 Radiographers filling the mammography screening gap, but where's the evidence?
Authors: N. Clerkin, C.F. Ski, P.C. Brennan, R. Strudwick
Journal: Radiography
Year: 2023

📄 An initial exploration of factors that may impact radiographer reporting of mammography images
Authors: N. Clerkin, C.F. Ski, P.C. Brennan, R. Strudwick
Journal: Radiography
Year: 2024

 

Bin Hu | Medicine and Dentistry | Best Researcher Award

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Mr. Bin Hu | Medicine and Dentistry | Best Researcher Award

Hubei University of Technology | China

Author Profile

Scopus

Early Academic Pursuits

Bin Hu embarked on his academic journey at Hubei University of Technology, specializing in computer vision. During his undergraduate studies, he demonstrated exceptional promise by authoring three papers, including a groundbreaking cell nucleus segmentation method published in a prestigious journal.

Professional Endeavors

Currently pursuing graduate studies, Bin Hu has amassed over 7 years of experience in computer vision. He has led research projects during his postgraduate studies and actively contributed to multidisciplinary collaborations, showcasing his ability to tackle diverse challenges.

Contributions and Research Focus

Bin Hu's research focuses on computer vision, with a particular emphasis on developing advanced segmentation methods for medical imaging. His recent work introduces the Double-stage Codec Attention Network, a novel approach for accurate nucleus segmentation from tissue images. This method leverages hierarchical feature extraction, feature selection units, and multi-scale deep feature fusion to achieve superior segmentation performance.

Accolades and Recognition

Bin Hu's contributions have garnered recognition both nationally and internationally. He holds two national patents for inventions in his field and has presented his research at esteemed conferences such as IEEE Transactions on Medical Imaging. His pioneering work has earned him awards and recognition.

Impact and Influence

Bin Hu's research has significant implications for clinical applications, particularly in the field of medical imaging. His innovative segmentation methods, such as DSCA-Net, outperform state-of-the-art models and demonstrate excellent efficiency in generating predictive images. His contributions have the potential to advance the field of computer vision and improve medical diagnosis and treatment.

Legacy and Future Contributions

Bin Hu's expertise in computer vision and his practical problem-solving skills position him as a valuable contributor to innovative projects in both academic and industrial settings. His dedication to advancing research in medical imaging underscores his commitment to making meaningful contributions to society. As he continues his academic and professional journey, Bin Hu aims to further expand his research portfolio and drive advancements in computer vision technology.

Notable Publications

DSCA-Net: Double-stage Codec Attention Network for automatic nuclear segmentation 2024

Focus Stacking with High Fidelity and Superior Visual Effects 2024

Banafshe Felfeliyan | Health Professions | Best Researcher Award

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Dr. Banafshe Felfeliyan | Health Professions | Best Researcher Award

University of Alberta | Canada

Author Profile

Scopus

Orcid

 

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, followed by a Master's focusing on coronary vessel segmentation. She then pursued a Ph.D. in Biomedical Engineering, specializing in medical imaging, from the University of Calgary, Canada. Her doctoral research delved into automatic quantification of osteoarthritis features using deep learning techniques.

Professional Endeavors

Dr. Felfeliyan's professional career has been marked by significant contributions in the field of medical imaging and artificial intelligence. She served as a Computer Research Engineer at the McCaig Institute, University of Calgary, where she worked on bone segmentation using deep learning. Later, she transitioned to the role of a Postdoctoral Research Fellow at the Radiology & Diagnostic Imaging Department, University of Alberta, leading projects focused on AI-driven MRI biomarker profiling for osteoarthritis.

Contributions and Research Focus

Her research primarily revolves around the intersection of medical imaging and artificial intelligence, with a focus on automated AI biomarker extraction, machine learning, deep learning, and domain adaptation. Dr. Felfeliyan has made significant contributions to the development of novel algorithms and methodologies for medical image segmentation and analysis, particularly in the context of osteoarthritis diagnosis and assessment.

Accolades and Recognition

Dr. Felfeliyan's outstanding contributions have been recognized through numerous honors and awards, including the Alberta Innovates Postdoctoral Recruitment Fellowship, Biomedical Engineering Research Excellence Award, and the AI Week Talent Bursary from the Alberta Machine Intelligence Institute. She was also honored as one of the top 15 young female scientists in Canada at the SCWIST Symposium.

Impact and Influence

Her research outputs, comprising publications in prestigious journals and presentations at international conferences, demonstrate the significant impact of her work on the scientific community. Dr. Felfeliyan's innovative approaches to medical image analysis have the potential to revolutionize clinical diagnosis and treatment planning, ultimately improving patient outcomes and healthcare delivery.

Legacy and Future Contributions

Dr. Felfeliyan's legacy lies in her pioneering work at the intersection of biomedical engineering and artificial intelligence, shaping the future of medical imaging and diagnostics. Her commitment to mentorship and teaching ensures the continuity of her legacy by nurturing the next generation of researchers and engineers. As she continues her academic journey, Dr. Felfeliyan remains dedicated to advancing the frontiers of knowledge and making meaningful contributions to healthcare innovation.

Notable Publications

OMERACT validation of a deep learning algorithm for automated absolute quantification of knee joint effusion versus manual semi-quantitative assessment 2024