MEDVISION: Medical Imaging Analytics Platform

SHORT SUMMARY 

The Medical Imaging Analytics Platform provides a dedicated computational environment for developing, training, and validating deep learning models applied to medical imaging data. Built on NVIDIA RTX 6000 ADA and H100 NVL GPUs, MEDVISION is purpose-configured for the high memory and throughput demands of medical image processing, supporting standard imaging formats and pipelines used in clinical and research contexts. The platform supports a range of imaging modalities and analytical tasks including segmentation, classification, anomaly detection, and computer-aided diagnosis, using frameworks and architectures commonly applied in medical AI research. This testbed contributes to the CITADELS Framework by providing accessible, domain-focused infrastructure that supports the translation of medical imaging AI from research prototype to clinically relevant proof-of-concept.

HOSTING INSTITUTION AND PI INFO

Name of Host Organization NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa
Department or Lab MagIC (Information Management Research Center) – the NOVA Information Management School 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

APPLICATION CASES 

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.

POTENTIAL STAKEHOLDERS

Non-academic stakeholders

Industrial Partners, Startups, Professional Associations, SMEs, Government Bodies

Academic stakeholders

PhD students, MSc students, Researchers

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