Aristotle University of Thessaloniki

The Aristotle University of Thessaloniki is the largest university in Greece covering all disciplines. ( It is widely recognized as a vibrant center of learning which draws its inspiration from a long tradition of academic achievement. The School of Medicine is one of the four Schools of the Faculty of Health Sciences, at AUTH. It is one of the most important and well-established Schools of Greece, both quantitatively and qualitatively. The faculty of the School of Medicine is the staff of several hospitals as well as other units of the National Health System, and thus provides important social work. Τhe school offers more than 50 Postgraduate Programmes, and participates in numerous research programs. 

The Lab of Computing, Medical Informatics and Biomedical Imaging Technologies (LOMI) has a continuous role in Medical Informatics and Biomedical Engineering research and development since 1990. The Lab runs undergraduate courses around Medical Informatics, ICT for Health and AI in the Faculty and contributes in various BME curricula at AUTH. Its researchers collaborate with the University Clinics of Aristotle University and the Medical School at large, towards developing and piloting digital health solutions and have been active in the BME and digital health R&D in national and EU projects. 

Within the Lab, our team (BioAsys), led by Ioanna Chouvarda, is mainly interested in the development of biomedical data analysis methods & integrated data-driven approaches, the exploration of machine learning and AI for health-related predictive models and precision medicine, the incorporation of AI in connected health applications & systems medicine.  Recent projects: INCISIVE, CHAMELEONS, CHESS, MILORD,  PATHWAY, AEGLE, WELCOME.

The team collaborates with the Radiology Lab and the Nuclear Medicine Lab at AHEPA University Hospital, as well as other clinicians and researchers from other Hospitals in Thessaloniki.

Role of institution in the project

In EUCAIM,  AUTH brings the INCISIVE experience, as well as a long experience in biomedical data (biological, imaging, clinical), Machine Learning, XAI and validation of methods.

AUTH will contribute to WP5  data models/standards and data quality tools and experience, to WP6 with analysis pipelines (preprocessing, radiomics and ML/DL, explainable and trustworthy AI), and to WP2/WP7 with requirements and collaboration with data providers from AHEPA University Hospital.

Ioanna Chouvarda

Ioanna Chouvarda (F) studied Electrical and Computer engineering, at AUTH and completed her PhD on cardiac system complexity in the School of Medicine. She is Associate Professor in Medical Informatics and Biomedical Data Analysis. Previously, she has been a collaborating researcher with CERTH, visiting researcher at VTT Finland and at the Polytechnic School of Milan. She teaches undergraduate and graduate level courses around biomedical data management and analysis, also oriented to interdisciplinary groups, and has supervised numerous Msc and PhD students. IC has been actively involved in biomedical research for more than 25 years. Within the Lab, she leads the BiOasys team with key objectives the development of data analysis & data-driven methods, and their application in connected health technologies and systems medicine. She has been involved in numerous research projects (EU/national), as well as in EU Cost Actions for scientific networking. She has been an evaluator of national and EU research proposals and projects. She has served as board member in IEEE WIE and in EMB Greek chapter since 2020, and associate editor in conferences and journals. She has authored 94 articles in journals and over 200 articles in conferences/book chapters. [email protected],

Olga Tsave

Olga Tsave, is a postdoctoral researcher (Laboratory of Computing, Medical Informatics and Biomedical - Imaging Technologies and Laboratory of Inorganic Chemistry and Advanced Materials, Department of Chemical Engineering, A.U.Th). She graduated of the School of Biology (A.U.Th.), with a focus on Molecular Biology, Genetics and Biotechnology, and holds two M.Sc.s in Applied Genetics and Biotechnology (A.U.Th. 2012-2015) and in Medical Informatics, (A.U.Th. 2016-2019). Her research interests include the development of models of biological processes and metabolomics, structure-activity relationship of novel materials in disease -therapy, metallo-induced processes in the pathogenesis of metabolic and neurodegenerative diseases, medical imaging, development of models for assessing exposure to xenobiotic agents (in vitro, ex vivo, in vivo), and electrophysiology. Her research work includes participation in national-international conferences (64 papers), and full articles in international scientific journals of high IF (28 papers). Email: [email protected], [email protected].

Alexandra Kosvyra

Alexandra Kosvyra (female) graduated from the Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki in 2014 and received her MSc in Medical Informatics, School of Medicine, Aristotle University of Thessaloniki in 2018. She is currently a PhD student in the same Department in the area of Machine Learning in Genomics. She is a member of the Lab of Medical Informatics, Aristotle University of Thessaloniki since 2017 and has served as a junior researcher in the Institute of Applied Biosciences, Centre for Research & Technology Hellas (Greece) from 2017 to 2018. She has participated as research associate in 2 national (Greek) and 3 EU-funded research projects and has co-authored 12 peer-reviewed papers in international journals and conference proceedings. Email: [email protected]

Dimitris Filos

Dimitris Filos (M), Electrical Eng PhD, is a researcher in the lab of Computing, Medical Informatics and Biomedical Imaging Technologies, in the medical school of the Aristotle University of Thessaloniki, Greece. He is involved in the field of Digital Health and Medical Informatics for more than 10 years. His main scientific areas include biomedical signal processing, physiological systems and behavioural modelling, machine learning as well as data analytics and decision making. He has authored or co-authored several papers published in international journals, conference proceedings and book chapters. More info at Email: [email protected].

Join the EUCAIM Consortium

Open Call for New Beneficiaries

We’re inviting new partners to enhance our pan-European infrastructure for cancer images and artificial intelligence.

Whether you’re a data holder with valuable cancer images or an innovator developing AI tools for precision medicine, this is your chance to contribute to a groundbreaking project.

Apply by 10 June 2024!

Open Call Webinar

We recently hosted a webinar with more details for prospective applicants to the open call. A recording is available.

Our open Call for new collaborators
launches in April 2024

Opportunities for data holders & AI developers to contribute await! Let‘s join forces to enhance cancer diagnosis and treatment

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March 14, 10:00-11:30 aM CET



Explore the potential for AI-driven cancer care advancements!
Learn how to access and utilize our federated cancer image repository. The webinar is for AI Innovators & Data Providers interested in the platform and will feature an introduction to EUCAIM & Cancer Image Europe and a demonstration of data exploration & access.

Survey Invitation

Join Leading Experts In Shaping AI In Cancer

EUCAIM is looking for your feedback! We have recently published a Stakeholder Survey in order to reach out to potential end-users and stakeholders. We believe that your insights could significantly contribute to understanding the expectations of potential users and identifying the essential aspects that stakeholders find crucial for future engagement and collaboration with the platform.

Therefore, we would like to invite you to participate in the Stakeholder Survey about the Cancer Image Europe platform.

Completing the survey will take approximately 10 minutes. Your participation is crucial to the success of this project, and we deeply appreciate your expertise in shaping the future of cancer imaging and treatment.