Special Session
Special Session on
Data- and thEory-driven approaChes to personalized braIn medicine: from diagnosis to treatment -
DECIDE
2025
20 - 22 February, 2025 - Porto, Portugal
Within the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSTEC 2025
* CANCELLED *
CHAIR
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Vassilis Cutsuridis
University of Plymouth
United Kingdom
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Brief Bio
Vassilis Cutsuridis is an Associate Professor in Computer Science, and a member of the Machine Learning research group at the University of Lincoln. He holds a BSc and MSc in Maths and Physics, an MA in Cognitive and Neural Systems, and a PhD in Computational Neuroscience. He is an active member of UK’s Applied Vision Association Society, the British Oculomotor Group, EU’s Convergent Science Network of Bio-mimetic and Bio-hybrid Systems since 2010 and of European Network for the Advancement of Artificial Cognitive Systems. His research spans brain inspired artificial intelligence including neural computation, cognitive modelling, bio-machine learning and biosignal analysis. He has developed large scale connectivity-based models of Parkinson’s disease, biomimetic learning rules and neural network models of learning and memory, deep neurocognitive models of perception-cognition-action in robots, and behavioural models of eye movements in neurodegenerative diseases such as Huntington’s disease, schizophrenia and OCD.
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SCOPE
Deciding the right diagnosis, the best treatment and predicting the evolution of the disease is of paramount importance in personalized medicine. Diseases are dynamic and very heterogeneous. Etiologies are complex and often several hypotheses are needed to explain their pathogenesis. This is because studies carried out isolate the effects of a single mechanism and not the interaction of many mechanisms. This leads to a set of conflicting results difficult to interpret. Data-driven (ML/DL) and theory driven (Dynamical models) approaches dealing with diverse data are increasingly used in medicine. Bridging the gap between data- and theory-driven approaches is the central theme of the special session. Real progress in personalized medicine can only be made via such cross-disciplinary interactions.
TOPICS OF INTEREST
Topics of interest include, but are not limited to:
- Computational/mathematical models of drug and stimulation treatments and therapies
- Data processing, image and signal reconstruction and enhancement and cross-modality synthesis
- Development of a culture of mathematical and computational modelling of brain diseases that will benefit those in clinical practice
- Extraction of biomarkers from ‘omics’, signal and imaging data
- Machine learning algorithms for prediction of disease evolution
- Machine learning algorithms in disease detection and diagnosis
- Multi-scale, multi-level models of disease understanding
- Translation research on the dynamics of diseases into treatments for age related illnesses
IMPORTANT DATES
Paper Submission:
December 27, 2024 (extended)
Authors Notification:
January 14, 2025
Camera Ready and Registration:
January 22, 2025
SPECIAL SESSION PROGRAM COMMITTEE
Available soon.
PAPER SUBMISSION
Prospective authors are invited to submit papers in any of the topics listed above.
Instructions for preparing the manuscript (in Word and Latex formats) are available at: Paper Templates
Please also check the Guidelines.
Papers must be submitted electronically via the web-based submission system using the appropriated button on this page.
PUBLICATIONS
After thorough reviewing by the special session program committee, all accepted papers will be published in a special section of the conference proceedings book - under an ISBN reference and on digital support - and submitted for indexation by SCOPUS, Google Scholar, DBLP, Semantic Scholar, EI and Web of Science / Conference Proceedings Citation Index.
SCITEPRESS is a member of CrossRef (http://www.crossref.org/) and every paper is given a DOI (Digital Object Identifier).
All papers presented at the conference venue will be available at the SCITEPRESS Digital Library