Doohyun Park | Computer Science | Best Researcher Award

Posted on 21/09/202421/09/2024 by Book of Award

Dr. Doohyun Park | Computer Science | Best Researcher Award

VUNO Inc. | South Korea

Author Profile

Early Academic Pursuits 🎓

Dr. Doohyun Park embarked on his academic journey at Yonsei University, where he earned his Bachelor's degree in Electrical and Electronic Engineering (2012-2016). His deep interest in medical applications of technology led him to pursue a Ph.D. at the same institution. His doctoral thesis focused on artificial intelligence-based preoperative prediction of axillary lymph node metastasis in breast cancer using whole slide images, which showcases his commitment to integrating AI in healthcare. His academic path laid the foundation for his future contributions to biomedical research and medical image analysis.

Professional Endeavors 💼

Dr. Park’s professional career is marked by his significant role at VUNO Inc., where he is part of the Lung Vision AI team. His work involves the development of computer-aided detection and diagnosis (CADe/CADx) on lung CT, focusing on innovative solutions for lung health. He has also worked on projects assessing the severity of COVID-19 and anomaly detection in spine CT. His expertise in the intersection of AI and healthcare has positioned him as a key contributor to advanced diagnostic technologies, reflecting his ability to bridge academia and industry.

Contributions and Research Focus 🔬

Dr. Park's research interests are centered around biomedical and clinical research, with a particular emphasis on computer-aided detection, diagnosis, and medical image analysis. He has published numerous papers on topics ranging from deep learning-based joint effusion classification to the development of AI models for lung cancer screening. His research has garnered recognition in top-tier journals, reinforcing his role in advancing AI applications in healthcare. He also holds multiple international and domestic patents related to prognosis prediction using image features, underscoring his contributions to the global research community.

Accolades and Recognition 🏆

Dr. Park’s outstanding contributions to medical image analysis have earned him several prestigious awards. Notably, he won the Best Paper Award at the 2023 MICCAI Grand Challenge for Aorta Segmentation and secured third place in the competition. His academic excellence has also been recognized through scholarships, including the Brain Korea 21 Scholarship and various research and teaching assistant scholarships during his time at Yonsei University. His consistent track record of achievements highlights his dedication to both research and education.

Impact and Influence 🌍

Dr. Park's work has had a profound impact on the field of medical AI, particularly in improving diagnostic tools for lung and breast cancer. His development of cutting-edge algorithms for image analysis has the potential to revolutionize early detection and prognosis in clinical settings. His invited talks at high-profile forums like the Global Engagement & Empowerment Forum on Sustainable Development (GEEF) further showcase his influence on global health initiatives, particularly in the context of the United Nations' Sustainable Development Goals.

Legacy and Future Contributions ✨

As Dr. Park continues his career, his legacy is being built on the foundations of innovation, interdisciplinary collaboration, and a commitment to improving healthcare outcomes. His ongoing projects, including AI-based lung cancer screening and prognosis prediction for adenocarcinoma, promise to shape the future of diagnostic medicine. With a robust portfolio of patents, publications, and collaborative research, Dr. Park is poised to make lasting contributions to both academic and clinical communities, further solidifying his role as a pioneer in medical AI.

Publications

📝 Deep Learning-Based Joint Effusion Classification in Adult Knee Radiographs: A Multi-Center Prospective Study
Authors: Hyeyeon Won, Hye Sang Lee, Daemyung Youn, Doohyun Park, Taejoon Eo, Wooju Kim, Dosik Hwang
Journal: Diagnostics
Year: 2024

📝 M3F: Multi-Field-of-View Feature Fusion Network for Aortic Vessel Tree Segmentation in CT Angiography
Authors: Yunsu Byeon, Hyeseong Kim, Kyungwon Kim, Doohyun Park, Euijoon Choi, Dosik Hwang
Journal: Book Chapter
Year: 2024

📝 Weakly Supervised Deep Learning for Diagnosis of Multiple Vertebral Compression Fractures in CT
Authors: Euijoon Choi, Doohyun Park, Geonhui Son, Seongwon Bak, Taejoon Eo, Daemyung Youn, Dosik Hwang
Journal: European Radiology
Year: 2023

📝 Development and Validation of a Hybrid Deep Learning–Machine Learning Approach for Severity Assessment of COVID-19 and Other Pneumonias
Authors: Doohyun Park, Ryoungwoo Jang, Myung Jin Chung, Hyun Joon An, Seongwon Bak, Euijoon Choi, Dosik Hwang
Journal: Scientific Reports
Year: 2023

📝 Importance of CT Image Normalization in Radiomics Analysis: Prediction of 3-Year Recurrence-Free Survival in Non-Small Cell Lung Cancer
Authors: Doohyun Park, Daejoong Oh, MyungHoon Lee, Shin Yup Lee, Kyung Min Shin, Johnson SG Jun, Dosik Hwang
Journal: European Radiology
Year: 2022

Debapriya Banik | Computer Science | Best Researcher Award

Posted on 16/07/202416/07/2024 by Book of Award

Dr. Debapriya Banik | Computer Science | Best Researcher Award

ICFAI University | India

Author profile

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Early Academic Pursuits

Dr. Debapriya Banik embarked on his academic journey with a solid foundation in secondary and higher secondary education at Holy Cross School in Agartala, achieving commendable scores in both ICSE and ISC examinations. His passion for Computer Science led him to pursue a Bachelor of Technology (B.Tech) in Computer Science and Engineering at the National Institute of Technology Agartala, where he graduated with a CGPA of 7.59. His pursuit of higher education continued at Tripura University, where he excelled in his Master of Technology (M.Tech) in Computer Science and Engineering, earning a gold medal for securing the highest percentage. Dr. Banik's academic endeavors culminated in a Doctor of Philosophy (Ph.D.) in Computer Science and Engineering from Jadavpur University, where he focused on developing computer-aided techniques for the early prediction of colorectal cancer based on diagnostic image analysis.

Professional Endeavors

Dr. Banik's professional career is marked by diverse and impactful roles in academia and industry. He began his career as a consultant at Polaris Financial Technology Ltd in Chennai, gaining valuable industry experience. Transitioning to academia, he served as a Junior Research Fellow (JRF) on a DBT Twinning Project at Jadavpur University, working on techniques for pain management and breast cancer using IR imaging. He then became a Senior Research Fellow (SRF)-Direct at Jadavpur University under the sponsorship of the Council for Scientific and Industrial Research, Government of India. His academic career continued to flourish as he took on roles as an Assistant Professor at Techno India in Kolkata and later at ICFAI University in Tripura, where he currently teaches in the Department of Computer Science & Engineering.

Contributions and Research Focus

Dr. Banik's research has significantly contributed to the field of computer science, particularly in medical image analysis. His Ph.D. thesis focused on developing computer-aided techniques for the early prediction of colorectal cancer, leveraging diagnostic image analysis. Additionally, he has worked on the design and development of techniques for pain management and breast cancer using IR imaging. His innovative research approaches have not only advanced the field but also provided practical solutions for critical medical challenges. Dr. Banik's work is characterized by a strong focus on applying machine learning and computational techniques to enhance diagnostic accuracy and efficiency.

Accolades and Recognition

Dr. Banik's academic excellence and research contributions have earned him numerous awards and recognitions. He was awarded a gold medal for his outstanding performance in M.Tech at Tripura University. He received the DST-Inspire fellowship from the Department of Science and Technology, Government of India, and the North Eastern Council (NEC) Scheme fellowship for postgraduate studies. Dr. Banik secured the first position in the Workshop on Machine Learning for Medical Image Analysis (WMLMIA) - Fetal Ultrasound Censor (FUC) Grand Challenge, organized by the Department of Electrical Engineering at IIT Kharagpur. Additionally, he had the opportunity for a short-term research visit to the Medical University of Vienna, Austria, sponsored by DST, Government of India, where he worked on developing a computer-assisted diagnosis system for segmenting and detecting abnormalities and diseases under the supervision of Prof. Christian Kollman.

Impact and Influence

Dr. Banik's work has had a profound impact on the field of medical image analysis, particularly in the early detection and management of diseases such as colorectal and breast cancer. His research has paved the way for more accurate and efficient diagnostic tools, improving patient outcomes and contributing to the advancement of medical technology. As an educator, he has influenced and mentored numerous students, fostering a new generation of computer science professionals who are equipped with cutting-edge knowledge and skills.

Legacy and Future Contributions

Dr. Banik's legacy lies in his dedication to advancing the field of computer science through innovative research and his commitment to education. His work continues to inspire researchers and students alike, and his contributions to medical image analysis have set a high standard for future research in the field. Looking ahead, Dr. Banik aims to further his research on computer-aided diagnosis systems, exploring new applications and techniques to address emerging challenges in healthcare. His future contributions are expected to continue making significant strides in improving diagnostic accuracy and patient care through advanced computational methods.

Notable Publications

Robust medical and color image cryptosystem using array index and chaotic S-box 2024

2pClPr: A Two-Phase Clump Profiler for Segmentation of Cancer Cells in Fluorescence Microscopic Images 2023 (2)

LwMLA-NET: A Lightweight Multi-Level Attention-based NETwork for Segmentation of COVID-19 Lungs Abnormalities from CT Images 2022 (39)

Bin Hu | Medicine and Dentistry | Best Researcher Award

Posted on 18/04/202418/04/2024 by Book of Award

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

Focus Stacking with High Fidelity and Superior Visual Effects 2024

Banafshe Felfeliyan | Health Professions | Best Researcher Award

Posted on 19/02/202420/02/2024 by Book of Award

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.