With the ever-growing demands on the healthcare system on a global scale, deep learning and computational intelligence has become increasingly prevalent and seen as a crucial tool for not only improving the quality of care (increasing clinical decision and treatment accuracy and consistency) but also greatly improve the efficiency of care, allowing more patients to be assessed and treated.This session aims to present recent advances in medical image processing using deep learning (e.g., convolutional neural networks, auto encoders, generative adversarial networks, capsule networks, deep belief networks) and computational intelligence (e.g., multilayer perceptron, neuro-fuzzy, genetic algorithms). The objective of this special session is to advance scientific research of computational intelligence in medical image analysis. The session is going to foster the debate within the clinical domain of the most important breakthroughs in artificial intelligence techniques applied to medical imaging, boosting emerging transdisciplinary fields such as computational radiology and computational pathology.Topics of interest include, but are not limited to:
- Deep learning for clinical diagnosis
- Artificial Intelligence and Deep Learning for Computational Pathology and Radiology
- Detection and Discovery of Predictive and Prognostic Tissue Biomarkers
- Medical Image Reconstruction and Super-Resolution
- Computational Intelligence for Surgical Planning and Guidance
- Deep predictive models for prognosis and risk assessment
- Deep learning for clinical decision support for treatment selection and planning
- Data-driven biomarker discovery from medical imaging data for a disease with deep neural networks
- Image-driven natural language processing for clinical structured and unstructured report analytics with deep neural networks
- Medical image reconstruction using deep neural networks
Dr. Kun Zhang(Member, IEEE) received the M.S. degree in control engineering from Zhejiang University, Hangzhou, China, and Ph.D. degree in control theory and control engineering from Shanghai University, Shanghai, China. From 2014 to 2015, and 2017-2018 he had been a Visiting Scholar and Research Fellow with the School of EECS, Queens University Belfast, UK, respectively. He is currently a Professor with the School of Electrical Engineering, Nantong University, Nantong.
His current research interests include the areas of artificial intelligence, machine vision, image processing, and their applications in bio-image and industry area.
Prospective authors are invited to submit full-length papers before the submission deadline through the online submission system at https://easychair.org/conferences/?conf=lsms2021icsee2021.Further, proposals for special sessions within the technical scopes of the conference are most welcome. Papers submitted for special sessions will be peer-reviewed with the same criteria as used with regular papers. A special session proposal should include the session title, a brief description, and the names, contact details and bio-sketch of the organizers.
See also the Author's kit for paper format.
Accepted papers will be published in the Springer Communications in Computer and Information Science (CCIS) proceedings (EI Compendex). Some high-quality papers will be recommended for possible publication in SCI indexed international journals after expansion and further review, such as Journal of Modern Power Systems and Clean Energy (MPCE), Transactions of the Institute of Measurement and Control (TIMC), Enterprise information system (EIS), Frontiers In Energy Research, Cognitive Computation, Journal of Control and Decision, Advances in Manufacturing (AIM), etc.