The mission of the Task Force on Biomedical Engineering Applications is to promote the research, development, education and understanding of the applications of computational intelligence to biomedical engineering.
Biomedical Engineering (BE) aims to apply engineering principles to biomedical systems. A biomedical system may be very broadly defined as any engineered device or apparatus that interacts with a biological entity to achieve a medical purpose. Examples of biomedical systems include medical diagnostic systems, theranostics, medical imaging, advanced prosthetics, biosensors, telemedicine and many more. This is a fast-growing research area which is throwing up many complex problems as researchers grapple with the need to model complex systems and interpret massive biological and medical datasets. Computational Intelligence (CI) has proven highly successful in its application to more traditional engineering disciplines. CI approaches contribute in a wide variety of ways including design, mathematical, probabilistic and statistical modelling, control, automation and optimisation, safety and reliability, system identification, monitoring and fault detection. There is therefore strong motivation to bring this experience to bear on the considerably more complex research challenges posed by BE.
The scope of the task force will be to identify, support and encourage all areas where CI can have a major impact. Currently applications of CI to BE are varied and disparate. They include data mining, probabilistic modelling, pattern recognition, classification, optimisation, and parameter estimation applied to a wide range of medical conditions. A central focus for the task force will be to discover common approaches and problems, clearly identify the strengths CI brings and create communities and standards focussed on major application areas with potential for real impact and improvement of BE. This will bring together existing researchers and also encourage other CI researchers into the area.
John McCall obtained his PhD in Mathematics at the University of Aberdeen. His research for the past 18 years has focussed on the study and analysis of a range of naturally-inspired metaheuristics (EA, EDA, PSO, ACO, ANN etc..) and their application to learning and optimisation problems. Major themes of his research include medical treatment optimisation, particularly cancer-related, the development and analysis of novel metaheuristics, particularly Markov-network EDAs, and probabilistic modelling for optimisation and learning. Application areas of this research include medical decision support, oil and gas operations, analysis of biological sequences, scheduling, image analysis and bio-control. Prof. McCall is currently Digital Technologies Theme Leader at the IDEAS Research Institute of Robert Gordon University, Aberdeen.
Lilian Tang received B.Eng. and M.Eng. degrees from Northeastern University, P.R. China, and a Ph.D. degree in Medical Informatics from the University of Cambridge, UK. She has 15 years of experience in applying machine vision to image and video analysis, with a particular focus on the medical domain. During this time she has worked with Addenbrookes Hospital, the Royal Surrey Hospital, the Royal Botanic Gardens, Kew and Moorfields Eye Hospital on a wide range of projects. She is currently a lecturer at the University of Surrey.