We are pleased to announce that the research team from the Faculty of Science and Mathematics has published a scientific paper titled “Data augmentation for fuselage panel inspection via 3D point cloud segmentation” in the Journal of Electronic Imaging. The paper presents advanced data augmentation methods to improve fuselage panel inspection using 3D point cloud segmentation, contributing to more accurate and reliable AI-based inspection systems. The research was enabled by access to the Leonardo HPC supercomputing resources, granted through the EuroCC2 project, which allowed the team to process large datasets and develop high-performance models efficiently. More info at: https://doi.org/10.1117/1.JEI.35.3.031202
Podgorica, 13 February 2026 – The Faculty of Medicine at the University of Montenegro hosted a regional symposium dedicated to the application of High-Performance Computing (HPC) and Artificial Intelligence (AI) in healthcare and medical research.
The event was organized by NCC Montenegro, in collaboration with the Faculty for Information Systems and Technologies (UDG) and the Faculty of Medicine (UoM), within the framework of the EuroCC2 and EuroCC4SEE projects, with additional support from the AI-AGE research project.
Bringing together approximately 20 participants from healthcare institutions, academia, innovative companies, and regional partners from Bosnia and Herzegovina, the symposium aimed to strengthen collaboration and advance the adoption of AI and HPC technologies in the health sector.
From Vision to Implementation
The programme combined strategic presentations, regional cooperation sessions, and technical demonstrations, creating a comprehensive overview of the current state of HPC and AI in healthcare.
NCC Montenegro presented Montenegro’s role as a national reference point for HPC, High-Performance Data Analytics (HPDA), and AI development. The presentation traced the entire pipeline—from clinical and biomedical data collection to AI model development and HPC-accelerated deployment.
A central message of the event was clear: HPC in healthcare is not merely about computational speed. It enables rigorous validation, reproducibility, and scalable deployment of AI models in real clinical environments.
Use cases discussed during the symposium included radiology, digital pathology, cardiology, genomics, ICU monitoring, and public health forecasting
AI-AGE: Advancing Research on Ageing
A dedicated session focused on the AI-AGE project, which explores retinal fundus imaging as a potential biomarker for accelerated biological ageing.
The interdisciplinary team presented research results based on UK Biobank data and datasets collected in Montenegro. Findings indicate that the complexity of retinal microvascular networks may decline more rapidly in patients with chronic diseases, highlighting potential applications in early diagnosis and monitoring.
Speakers emphasized the importance of careful model validation, addressing training bias, and ensuring responsible clinical deployment. The discussion also highlighted the potential of EuroHPC resources to further strengthen research capacity and computational scalability
Technical Showcase: AI Solutions Already in Practice
One of the most dynamic parts of the symposium was the Technical Showcase, where companies from Montenegro and Bosnia and Herzegovina presented concrete AI and HPC-enabled healthcare solutions.
Among the showcased innovations were:
AI-powered colon cancer detection in digital pathology using deep learning on high-resolution histopathology slides
AI-driven IoT platforms supporting clinical decision-making and patient management
AI systems for Alzheimer’s disease care, including predictive digital twins and multimodal reasoning tools
HPC-supported computational simulations accelerating pharmaceutical drug development
A particularly valuable component of the session was the sharing of experiences from companies that successfully applied for and received EuroHPC computing resources. These examples demonstrated how access to supercomputing infrastructure directly enhances model development, testing, and product readiness.
Strengthening Regional Cooperation
The symposium also included a regional twinning workshop between NCC Montenegro and NCC Bosnia and Herzegovina.
The session focused on joint strategies for stakeholder engagement, cross-border resource sharing, and knowledge transfer. The discussion confirmed that the twinning model is an effective mechanism for strengthening the South-East European HPC ecosystem and facilitating access to European supercomputing infrastructure.
Such cooperation is particularly important as the region prepares for the next phase of European HPC initiatives and increasing alignment with the EU AI Act and broader digital strategies.
Addressing Systemic Challenges
The event concluded with an interactive panel discussion titled “Orchestrating the Ecosystem.” Participants addressed key challenges facing AI adoption in healthcare, including:
The healthcare data gap and fragmentation
Regulatory complexity, particularly in the context of the EU AI Act
The need for stronger partnerships between industry, academia, and healthcare institutions
While AI model architectures continue to mature rapidly, participants agreed that the primary bottlenecks lie in data heterogeneity, evaluation standards, and deployment constraints rather than algorithmic limitations.
Healthcare representatives acknowledged the growing importance of HPC and AI in medical research but emphasized the need to improve institutional readiness for strategic and sustainable adoption.
A Strategic Step Forward
The symposium concluded with a shared commitment to:
Position AI and HPC as strategic priorities in healthcare innovation
Continue expanding infrastructure and access to HPC resources
Invest in skills development and capacity building
Strengthen regional collaboration across South-East Europe
The event marked an important step in connecting research excellence, industrial innovation, and clinical practice—demonstrating that HPC-enabled AI in healthcare is no longer a future concept, but an emerging regional reality.
We are proud to highlight PAID MNE as a featured success story in the EuroCC2 & EuroCC4SEE Booklet — demonstrating how HPC is transforming financial analytics and algorithmic trading.
EuroCC2 & EuroCC4SEE Booklet
At the heart of PAID MNE innovations lies PAID-T (Price Action Intelligent Detection Trading) — a smart trading platform that leverages advanced algorithms and AI/ML to dynamically adapt to market movements, optimise investment strategies, and manage risk with higher precision. Traditional computing systems quickly reached their limits. To unlock the required performance, the team scaled their solution to the LUMI supercomputer, one of Europe’s most powerful HPC infrastructures. By enabling multinode execution and real-time task distribution, PAID MNE achieved over 1.2 million simulations in under 5 hours — a process that previously would have taken days. This acceleration enables processing billions of historical transactions in hours instead of days, rapid identification of critical market patterns and data-driven optimisation/ increased accuracy of trading strategies.
PAID MNE success story
This achievement, showcased through the EuroCC2/EuroCC4SEE project, demonstrates how supercomputing is becoming a powerful enabler of business innovation. PAID MNE’s journey is a clear example of how HPC and AI together can transform complex, critical data into faster, more profitable decisions.
High-Performance Computing (HPC) and Artificial Intelligence (AI) are increasingly moving beyond research laboratories into real clinical environments. Across Montenegro and the SEE region, promising AI solutions have been developed for medical image analysis, biomarker detection, and predictive diagnostics. The critical challenge today is ensuring their structured transition from research prototypes to validated, deployable tools within healthcare systems.
Please contact us for attendance, limited number of seats
This event addresses precisely that transition. It focuses on how HPC infrastructure, interdisciplinary collaboration, and coordinated ecosystem support can accelerate the integration of AI into everyday clinical practice. Particular attention will be given to available computational capacities, real-life use cases, and pathways toward sustainable deployment.
The event is organized as a joint initiative between NCC Montenegro and NCC Bosnia and Herzegovina, within the broader framework of EuroCC 2 and EuroCC4SEE. It also represents a form of cross-project pollination with the AI-AGE project, demonstrating how research-driven innovation can evolve into applied healthcare solutions through regional cooperation.
Collaboration between NCC Monteengro and NCC Bosnia and Herzegovina
Researchers, clinicians, innovators, and industry partners are invited to join the discussion, exchange expertise, and contribute to shaping the next steps for HPC- and AI-driven healthcare across Southeast Europe. The event is scheduled for Friday, 13 Feb 2026. Please contact us for further details.
The Institute of Hydrometeorology and Seismology of Montenegro successfully secured HPC access from the EuroHPC JU Development Call for their project titled: “HPC Development for Very-High-Resolution Atmospheric Reanalysis Using a Nonhydrostatic Mesoscale Model over Montenegro (1995–2024)”. The project aims to enhance mesoscale weather modeling capabilities using the WRF-NMM (Nonhydrostatic Mesoscale Model) and to investigate the scalability and performance of nonhydrostatic dynamic cores on state-of-the-art high-performance computing (HPC) architectures.
Through this initiative, the project was granted 4,000 node hours on the LUMI-C partition for a period of six months. The National Competence Centre (NCC) Montenegro provided support throughout the project application process.
Mr. Igor Ćulafić successfully defended his master’s thesis titled “Cross-lingual Transfer Learning in Large Language Models: Scaling Laws and Parameter-Efficient Fine-Tuning for Multilingual Applications.” His research provides a comprehensive study of cross-lingual transfer for the Montenegrin language, combining a custom V-shaped semi-automated book scanner, a YOLOv11 + Tesseract OCR pipeline, and the creation of 46,661 parallel paragraph pairs. Using LoRA fine-tuning on Qwen2.5-7B and Qwen3-30B—executed on the Leonardo EuroHPC supercomputer—the work demonstrates parameter-efficient adaptation (only 1.05% trainable parameters) and offers insights into model behavior in cultural understanding, script mixing, and analytical reasoning. This research was supported by NCC Montenegro team and made use of the HPC cluster and EuroHPC JU computational resources.
V-shaped book scanner prototype used to create datasets
ABSTRACT – This thesis presents a comprehensive study of Cross-lingual transfer learning in Large Language Models with a focus on parameter-efficient fine-tuning for the Montenegrinlanguage. The research integrates the development of a custom semi-automated book scanner with V-shaped design and a computer vision pipeline using YOLO v11 models and Tesseract OCR to digitize 5000 on Montenegrin and 40000 on English language, from public domain books, resulting in 46661 parallel paragraph pairs. Implementation of LoRA fine-tuning on Qwen2.5-7B and Qwen3-30B models was conducted on Leonardo HPC supercomputer, achieving memory efficiency with only 1.05% trainable parameters. Comparative analysis through a structured benchmark of ten progressively complex questions reveals limited but positive effects of fine-tuning, where larger models show better performance in cultural understanding and analytical tasks, while systematic analysis identifies specific problems such as script mixing and cultural inaccuracies that require specialized approaches.
Ms. Ivana Lalatović successfully defended her master’s thesis titled “Application of Explainable Artificial Intelligence in Medicine” at the Faculty of Information Systems and Technologies, University of Donja Gorica.
The defence took place in October 2025, and the thesis explored how modern XAI techniques—such as SHAP and LIME—can improve transparency and trust in AI models used for analysing the performance and reliability of medical respirators. The development, training, and testing of the machine learning and XAI workflows were supported by the high-performance computing (HPC) resources provided through the EuroCC initiative in Montenegro, enabling scalable data processing, faster experimentation, and reproducible analysis required for medical AI applications. Her work demonstrates how HPC-enabled explainability can strengthen the safety, reliability, and ethical use of AI in healthcare environments, contributing to the growing ecosystem of advanced AI research supported by NCC Montenegro.
SHAP utilisation
ABSTRACT – The need for explainable intelligent systems is growing along with the increase in artificial intelligence products used in everyday life. Explainable artificial intelligence (XAI) has experienced significant growth in the last few years. The reason for this is the wide application of machine learning, as well as deep learning techniques, which have led to the development of highly accurate models. However, they lack explainability and interpretability. This study explores the application of XAI methods in medical applications, with a particular focus on interpreting model decisions. SHAP and LIME methods were applied to interpret the model’s predictions, enabling the identification of key features that have the greatest influence on the model’s decisions. The results of this research confirm the importance of explainable artificial intelligence in critical domains such as medicine, where trust in AI systems must be based on understanding and verifiability of their decisions.
