TOSBI – Towards Scalable and Generalized Solutions in Biomedical Images (2025-2028)
The TOSBI project — “Towards Scalable and Generalized Solutions in Biomedical Images” — aims to develop deep learning methodologies that scale to large bioimage datasets and generalize across imaging modalities and laboratories, with a particular emphasis on annotation-efficient approaches and adapting foundation models to bioimage tasks.
Led by the Computer Vision and Pattern Discovery (CVPD) group at the University of the Basque Country (UPV/EHU), TOSBI addresses key bottlenecks in modern biomedical image analysis: scarcity of high-quality annotations, computational cost of state-of-the-art models, and limited cross-domain generalization.
🔬 Scientific Goals
- Design and evaluate annotation-efficient learning strategies (self-supervised, semi-supervised, few-/zero-shot) tailored to biomedical images.
- Improve scalability of model training and inference for large microscopy and histopathology datasets.
- Develop domain adaptation and domain generalization techniques to transfer models between imaging modalities, labs, or experimental conditions.
- Adapt and validate foundation models for downstream bioimage tasks (segmentation, detection, classification), including practical tooling for the community.
- Deliver open and reproducible software components and evaluation pipelines compatible with community platforms (e.g., BiaPy).
👥 Project Team
Principal Investigators:
- Dr. Fadi Dornaika (Ikerbasque Research Professor, UPV/EHU)
- Dr. Ignacio Arganda-Carreras (Ikerbasque Research Associate Professor, UPV/EHU, DIPC, Biofisika Institute)
Research Team:
- Dr. Urtzi Ayesta (Ikerbasque Research Professor, UPV/EHU)
- Dr. Nagore Barrena (Assistant Professor, UPV/EHU)
- Dr. Unai Elordi (Assistant Professor, UPV/EHU)
Work Team / Collaborators:
- Aitor González-Marfil (PhD student, CVPD)
- Estibaliz Gómez de Mariscal (Postdoctoral researcher, Portugal)
- Daniel Franco-Barranco (Postdoctoral researcher / collaborator, UK)
- Dr. Jinan Charafeddine (De Vinci Higher Education, France)
- Dr. Fares Bougourzi (University of Salento, Italy)
- Dr. Donglai Wei (Boston College, USA)
- Software engineer (SE) (to be hired — WP8)
- PhD candidate (FPI) (to be recruited — linked FPI contract)
For the complete list of roles and the contribution of each member, see the project application PDF linked below.
🕒 Timeline
- Start: September, 1 2025
- Duration: August, 31 2028
💰 Funding
- Funded by the Spanish Ministry of Science, Innovation and Universities (MCIU) under the Generación de Conocimiento 2024 (Proyectos de Investigación No Orientada) program, MICIU/AEI /10.13039/501100011033/, and by FEDER, EU.
- Total funding: 143,250.00€ (including indirect costs)
- Reference code: PID2024-157485NB-I00
📄 Project documentation
- Related PhD announcement.
📬 Contacts
- For project-specific enquiries: Dr. Fadi Dornaika and Dr. Ignacio Arganda-Carreras
Ignacio Arganda-Carreras
Ikerbasque Research Associate Professor
My research interests include image processing, computer vision, and deep learning for biomedical applications.
Fadi Dornaika
Ikerbasque Research Professor
Ikerbasque Research Professor with expertise in computer vision, machine learning, and pattern recognition.
Nagore Barrena
Assistant Professor
My research focuses on computer vision and deep learning, with applications to biomedical and natural images.
Unai Elordi
Assistant Professor
My research interests include computer vision, pattern recognition, and artificial intelligence for intelligent video analytics.
Aitor González-Marfil
PhD Student
My research focuses on generalizable deep learning methods for computer vision.
Daniel Franco-Barranco
Postdoctoral Researcher
My primary focus is on the development of deep learning solutions for the segmentation of organelles in large-scale and multimodal electron microscopy images.