@@ -1,6 +1,6 @@
 ---
-title: "TOSBI – Towards Scalable and Generalized Solutions in Biomedical Images (2025-2028)"
-summary: TOSBI develops scalable and generalizable deep learning methods for biomedical image analysis, focusing on limited annotation, computational efficiency and robust transferability.
+title: "TOSBI – Towards Scalable and Generalized Solutions in Biomedical Images (2025–2028)"
+summary: TOSBI develops scalable and generalizable deep learning methods for biomedical image analysis, with a focus on limited annotation, computational efficiency and robust transferability.
 date: 2025-09-01
 authors: ["Ignacio Arganda-Carreras", "Fadi Dornaika", "Nagore Barrena", "Unai Elordi", "Aitor González-Marfil", "Daniel Franco-Barranco"]
 draft: false
@@ -9,71 +9,77 @@ image:
   focal_point: "center"
   preview_only: false
 tags:
-  - Biomimage Analysis
+  - Bioimage Analysis
   - Deep Learning
   - Scalability
   - Domain Generalization
   - Foundation Models
 ---
 
-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.
+The **TOSBI** project — *Towards Scalable and Generalized Solutions in Biomedical Images* — aims to create deep learning methodologies that scale to large bioimage datasets and generalize across imaging modalities, laboratories and experimental settings. The project emphasizes annotation-efficient learning and the adaptation of foundation models to biomedical imaging 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.
+Led by the **Computer Vision and Pattern Discovery (CVPD)** group at the **University of the Basque Country (UPV/EHU)**, TOSBI addresses central challenges in modern biomedical image analysis: limited high-quality annotations, the computational cost of state-of-the-art models, and the difficulty of achieving robust 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).
+### 🔬 Scientific Goals
 
-### 👥 Project Team
+- Develop and benchmark **annotation-efficient learning strategies** (self-supervised, semi-supervised, few-shot and zero-shot) tailored to biomedical imaging.
+- Improve **scalability** of training and inference workflows for large microscopy and histopathology datasets.
+- Create **domain adaptation and domain generalization** methods enabling models to transfer across imaging modalities, laboratories or acquisition conditions.
+- Adapt and validate **foundation models** for downstream bioimage tasks (segmentation, detection, classification), providing practical tools for the research community.
+- Release open and reproducible software components and evaluation pipelines fully compatible with community platforms such as **BiaPy**.
 
-- **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)
+### 👥 Project Team
 
-- **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)
+#### 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
+- **Start date**: 1 September 2025  
+- **End date**: 31 August 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**
+### 💰 Funding
 
+- Funded by the **Spanish Ministry of Science, Innovation and Universities (MCIU)** through the **Generación de Conocimiento 2024 (Proyectos de Investigación No Orientada)** program (**MICIU/AEI / 10.13039/501100011033** and **FEDER, EU**).  
+- **Total project funding**: 143,250 € (including indirect costs)  
+- **Reference code**: **PID2024-157485NB-I00**
 
 <img src="/assets/media/MICIU_Cofinanciado_AEI_logo.jpg" alt="Spanish State Agency for Research (AEI) Logo" width="500">
 
 ---
 
-### 📄 Project documentation
+### 📄 Project Documentation
 
-- Related [PhD announcement](/post/2025-09-30-phd-offer-tosbi/).
+- Related post: [PhD announcement](/post/2025-09-30-phd-offer-tosbi/)
 
 ---
 
 ### 📬 Contacts
 
-- For project-specific enquiries: [Dr. Fadi Dornaika](mailto:fadi.dornaika@ehu.eus) and [Dr. Ignacio Arganda-Carreras](mailto:ignacio.arganda@ehu.eus)
-
+For project enquiries, please contact:  
+- [Dr. Fadi Dornaika](mailto:fadi.dornaika@ehu.eus)  
+- [Dr. Ignacio Arganda-Carreras](mailto:ignacio.arganda@ehu.eus)