MISE
Collaborative platform for precision medicine, federated learning, and integrated clinical diagnostics.
Industrial Research
MISE
Collaborative platform for precision medicine, federated learning, and integrated clinical diagnostics.
Overview
MISE develops a collaborative technological platform for precision medicine and integrated clinical diagnostics. The project focuses on distributed clinical data aggregation, standardised information sharing, and AI methods that can learn from multiple clinical sources while preserving data locality.
ArCo's contribution is centred on the integration of AI algorithms and clinical applications, with emphasis on federated learning, model-parameter sharing, and consensus models for clinical research.
Research Directions
- Federated learning methods for distributed clinical research across multiple data holders.
- Consensus models that can be trained without centralising sensitive clinical data.
- Integration of AI algorithms with clinical applications for precision medicine workflows.
Related Publications
- Next-Gen Health: from Multimodal AI to Foundation Models2025foundation models generative AI multimodal learning medical imaging
- Texture-Aware StarGAN for CT data harmonization2025generative AI medical imaging
- Enhancing NSCLC Histological Subtype Classification: A Federated Learning Approach Using Triplet Loss2025radiomics oncology medical imaging
- Multimodal explainability via latent shift applied to COVID-19 stratification2024explainability multimodal learning COVID-19
- Cross-Modality Calibration in Multi-Input Network for Axillary Lymph Node Metastasis Evaluation2024radiomics oncology multimodal learning
- RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy2023radiomics oncology multimodal learning
- Early Experiences on using Triplet Networks for Histological Subtype Classification in Non-Small Cell Lung Cancer2023radiomics oncology medical imaging
- CNN-Based Approaches with Different Tumor Bounding Options for Lymph Node Status Prediction in Breast DCE-MRI2022radiomics oncology clinical prediction