Name of Host Organization

NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa

Department or Lab

MagIC (Information Management Research Center) - the NOVA IMS research and development center

Name of Building

Manuel Vilares Building

Physical Address

Campus de Campolide, 1070-312 Lisboa

Website Links

https://www.novaims.unl.pt/

Institutional contact name

Cristina Oliveira

Institutional contact email

magic@novaims.unl.pt

Principal Investigator Name

Professor Ian James Scott

Position / institutional role

Assistant Professor

ORCID

0000-0001-9699-4473

Email

iscott@novaims.unl.pt

TestBed Responsible Name
(if different from PI)

Funding source(s)
for TestBed’s acquisition

This testbed benefits from the resources of the NOVA Data & Analytics Hub (NOVA DAH), hosted at NOVA Information Management School (NOVA IMS) of Universidade NOVA de Lisboa. The work is supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project UID/04152/2025 (https://doi.org/10.54499/UID/04152/2025) (Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS); by the Plano de Recuperação e Resiliência (PRR) under projects UID/PRR/04152/2025 (https://doi.org/10.54499/UID/PRR/04152/2025) and EQUIPAR+2: UID/PRR2/04152/2025 (https://doi.org/10.54499/UID/PRR2/04152/2025); and by LISBOA2030 under project LISBOA2030-FEDER-01317500.

Application case:

Short description:

Medical Image Segmentation

Develop and benchmark deep learning segmentation models (e.g., U-Net and its variants) capable of delineating anatomical structures or regions of interest in medical images, supporting downstream diagnostic or treatment planning workflows.

Anomaly Detection in Imaging Data

Train convolutional and transformer-based models to identify abnormal patterns in medical imaging data, exploring approaches applicable to screening and triage scenarios where large volumes of images require automated prioritisation.

Multi-Modal Imaging Fusion

Experiment with architectures that combine information from multiple imaging modalities or pair imaging data with structured clinical metadata, exploring how complementary data sources can improve diagnostic model performance.

Benchmarking and Model Validation for Clinical Translation

Use the testbed to systematically evaluate model performance across diverse imaging datasets and patient subgroups, supporting the rigorous validation workflows required before clinical deployment under EU medical device regulations.

Non-academic stakeholders

Industrial partners, SMEs, Startups, Government bodies, Professional associations, Public agencies and municipalities

Academic stakeholders

MSc students, PhD students, Researchers, Visiting researchers, Seconded researchers

Other types of stakeholders

R&I support professionals, R&I infrastructure operators, Innovation intermediaries, Technology transfer actors

TRL application range

4

Internal academic research

Yes

Collaborative research with external academic partners

Yes

Contract research / Proof-of-Concept for industry

Yes

Pilot / DeepTech Deployment in operational environment

No

Training services (courses, workshops, certification)

Yes

Service provision (testing, benchmarking, validation)

Yes

Open access for walk-in users (e.g. open days / hackathons)

No

Other (Secondments / sponsored access for visiting researchers under project-based or institutionally approved arrangements)

Yes

Type of contractual relationship

Academic partner

Industrial partner

No contract (direct access)

No

No

Direct contract between parties
(e.g., research agreement)

Yes (See Note 1)

Yes (See Note 1)

Indirect contract between parties
(e.g., project framework)

Yes (See Note 1)

Yes (See Note 1)

Type of prerequisites

Description of prerequisites

Agreements

Confidentiality agreement for proprietary algorithms

In some cases (See Note 2)  

Data sharing agreement for datasets generated

In some cases (See Note 2)

IP agreements

In some cases (See Note 2)

Other 

In some cases (See Note 3 and 4)

Insurance

Users must have appropriate liability coverage through their home institution

Yes

Mandatory technical training

N/A

Recommended technical training

Recommended training on cluster operation, job submission, queuing in SLURM.

Mandatory safety requirements

No physical safety requirements apply. Users must follow institutional cybersecurity, data protection, GDPR and responsible AI rules where relevant.

Components:

Description:

Hardware

(physical equipment available in this TestBed)

1) NOVA DAH01 System Specifications:

a) CPU: 32-Core CPU - This processor provides a significant amount of processing power, enabling users to run multiple demanding tasks simultaneously, such as simulations, data processing, and other compute-intensive workloads.

b) GPU: 2 x Nvidia RTX 6000 ADA - These high-performance GPUs are designed to accelerate AI, HPC, and other GPU-accelerated workloads. With two RTX 6000 ADA GPUs, users can leverage massive parallel processing capabilities, handling large amounts of data and providing a substantial boost to performance.

c) Storage: 7TB NVMe Storage - This high-capacity storage solution provides rapid data access and transfer speeds, ideal for applications that require high-performance storage, such as data analytics, scientific simulations.

2) NOVA DAH02 System Specifications:

a) CPU: 112 CPU cores, providing a substantial amount of processing power for compute-intensive tasks. This will enable users to run multiple simulations, data processing, and other tasks concurrently.

b) GPU: 2 x Nvidia H100 NVL (Next-Generation High-Performance Computing) GPUs, which offer significant performance boosts for AI, HPC, and other GPU-accelerated workloads. The H100 NVL GPUs are designed to handle massive amounts of data and provide high-performance computing capabilities.

3) NOVA DAH WS includes:

a) 16 Lenovo Thinkstations P5 units, each equipped with an Intel(R) Xeon(R) W3-2423 processor, 32 GB DDRS-4800 MHz ECC memory, NVIDIA RTX(R) 2000 GPU with 16 GB GDDR6 (Ada Generation), and 1 TB PCIe Neg4 SSD.

b) Operating system Windows 11 Education,

c) Broad range of licensed and open-source software for data science, analytics, modelling, and visualisation, including but not limited to a broad range of licensed and open-source software for data science, analytics, modelling, and visualization, including but not limited to Python, R, Power BI, Tableau, SPSS, SAS, QGIS, ArcGIS, Docker, Anaconda, Visual Studio Code, and Zotero.

c) Storage: 500 TB of storage, providing ample space for storing large datasets, applications, and other data. This storage capacity will enable users to work with big data and store the results of their computations.

4) Others

Software

(needed to run
the TestBed)

1) SSH client

2) File transfer tools recommended

3) Apptainer runtime to test locally

Standards and regulations
(relevant for the safe and compliant operation of this TestBed)

N/A

Type

Short description:

Tutorial

How to connect to NOVA DAH (Tutorial NOVA DAH01)

Tutorial

How to connect to NOVA DAH (Tutorial NOVA DAH02)

Ethical and societal
aspect:

Short description:

Patient Privacy and GDPR Compliance

Medical imaging data is among the most sensitive categories of personal data under GDPR. Any use of patient-derived data on the testbed requires a lawful basis for processing, appropriate data anonymisation or pseudonymisation, and where applicable a Data Protection Impact Assessment (DPIA). Researchers are expected to follow NOVA IMS data governance procedures and applicable national health data regulations before commencing experiments.

EU AI Act and High-Risk AI Classification

AI systems intended for medical diagnosis or clinical decision support are explicitly classified as high-risk under Annex III of the EU AI Act. Models developed on MEDVISION for clinical applications must meet requirements for transparency, human oversight, robustness, and technical documentation, and may require conformity assessment prior to deployment.

Fairness and Bias in Medical AI

Deep learning models trained on medical imaging data can reflect biases present in the training population, leading to inequitable performance across demographic groups. Researchers using MEDVISION are encouraged to evaluate model performance across relevant subgroups and to document known limitations, in line with responsible AI principles and emerging clinical AI validation guidelines.

Support for European Health Research

By providing accessible infrastructure for medical imaging AI research, MEDVISION contributes to the European Health Data Space objectives, supporting the development of AI tools that can improve diagnostic efficiency, reduce clinician workload, and ultimately improve patient outcomes — without requiring dependency on large commercial platforms.

Data Availability and Synthetic Data

Given that access to real clinical imaging data is often restricted, the testbed encourages the use of publicly available benchmark datasets and synthetic data generation approaches where appropriate, enabling meaningful research while minimising privacy risk and avoiding assumptions about institutional data partnerships.