Deep Learning in Medical Imaging: Advancements, Challenges, and Clinical Impact

TOPICAL COLLECTIONS

Guest Editors

Tarun Agrawal

Jaypee Institute of Information Technology

Dr. Tarun Agrawal earned his PhD from the Department of Computer Science and Engineering at the National Institute of Technology (NIT) Hamirpur in India. He obtained his B.Tech and M.Tech degrees from the Zakir Hussain College of Engineering & Technology at Aligarh Muslim University in 2014 and 2016, respectively. His research interests include computer vision, medical imaging, machine learning, and deep learning. Dr. Agrawal has published several papers on medical image analysis in SCIE and Scopus-indexed journals and has presented his work at international conferences.

Prakash Choudhary

Central University of Rajasthan

Dr. Prakash Choudhary is an associate professor in the Department of Computer Science and Engineering and the dean of the Central University of Rajasthan, India. Previously, he was an assistant professor at the National Institute of Technology (NIT) Manipur and NIT Hamirpur. Dr. Choudhary earned his PhD from the Malaviya National Institute of Technology, Jaipur, in 2016. Additionally, he holds an M.Tech degree from the Visvesvaraya National Institute of Technology in Nagpur and a B.E. in Information Technology from the Government Engineering College in Ajmer (affiliated with Rajasthan University).Dr. Choudhary’s research interests include computer vision and image processing, specifically content-based image retrieval, automatic image annotation, medical imaging, biometrics, multilingual text recognition of Indian scripts, machine learning, and pattern recognition.

Vijay Kumar

Dr. B. R. Ambedkar National Institute of Technology Jalandhar

Dr. Vijay Kumar is an associate professor and the head of the Information Technology Department at Dr. B.R. Ambedkar National Institute of Technology Jalandhar, India. He previously served as an assistant professor at NIT Hamirpur. Dr. Kumar earned his PhD from the National Institute of Technology, Kurukshetra, Haryana, in 2015 and received an M.Tech from Guru Jambeshwar University of Science and Technology in 2008.His research interests focus on computer vision and image processing, particularly in medical imaging, machine learning, and pattern recognition. Dr. Kumar has significantly contributed to his field, publishing over 250 research papers in SCI, SCIE, Scopus, and ESCI-indexed journals and conferences. In 2024, he was recognized by Stanford University and Elsevier as one of the World’s Top 2% Scientists.

Prabhishek Singh

Bennett University

Dr. Prabhishek Singh, a senior member of IEEE and ACM, is an associate professor in the School of Computer Science, Engineering, and Technology at Bennett University (part of The Times Group) in Greater Noida, India. He has been with the university since July 2022. Recognized as one of the World’s Top 2% Scientists (2024) by Stanford University and Elsevier, Dr. Singh has made significant contributions to research and academia.He obtained his PhD in computer science from Babasaheb Bhimrao Ambedkar University (a Central University) in Lucknow, India, in 2018. He completed his M.Tech in Computer Science and Engineering from CDAC, Noida, in 2013 and earned his B.Tech in Computer Science and Engineering from A.K.T.U. in Lucknow in 2010.With over 10 years of experience in teaching and research, Dr. Singh has developed expertise in image processing, computer vision, deep learning, and machine learning. He has authored over 170 research papers published in SCI, SCIE, Scopus, and ESCI-indexed journals and conferences, significantly contributing to his field.In addition to his research work, Dr. Singh holds multiple editorial roles in prestigious journals. He is also a member of the editorial committee and a peer reviewer for numerous high-impact journals and conferences.

Collection Overview

The integration of deep learning into medical imaging has emerged as one of the most transformative trends in healthcare technology. Deep learning models, particularly convolutional neural networks (CNNs), transformers, and generative models, have shown state-of-the-art results in numerous medical image analysis tasks such as segmentation, classification, detection, registration, and image synthesis. Moreover, deep learning has enabled automated workflows that enhance diagnostic accuracy, reduce inter-observer variability, and improve clinical efficiency.

However, despite impressive progress, several challenges continue to hinder the large-scale adoption and clinical integration of these technologies. Issues such as limited annotated datasets, domain generalization, model interpretability, and compliance with ethical and regulatory standards remain open research areas.

This collection aims to explore the latest developments in the field, bringing together novel research, innovative methodologies, and real-world clinical applications that illustrate the potential of deep learning to revolutionize medical imaging.