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| title: CellsiLAB |
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| # CellsiLAB: Cells and Image Analysis |
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| **CellsiLAB** is the biomedical image analysis area of the **Codalab** research group at the **Universitat Politècnica de Catalunya (UPC)**. |
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| We work on **applied artificial intelligence**, focused mainly (though not exclusively) on **biomedical image analysis**. We develop **deep learning** and **computer vision** methods to support clinical decision-making, with a strong emphasis on **explainable** and **trustworthy AI**: we care not only about *what* a model predicts, but *why*. |
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| ## Research lines |
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| - **Explainable AI (xAI)** for biomedical imaging: new methodologies, evaluation strategies and clinical applications. |
| - **Computer vision for medical imaging**: classification, segmentation and quality assessment. |
| - **Generative and vision-language models** applied to medical data. |
| - **Federated learning** for robust multi-center generalization. |
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| ## Application domains |
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| - **Hematology**: blood cell classification, myelodysplastic syndromes, leukemia and red cell morphology. |
| - **Obstetrics**: fetal ultrasound quality assessment (nuchal translucency). |
| - **Pediatric ophthalmology**: retinopathy of prematurity. |
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| ## Research projects |
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| - **Explainable deep learning in medical image analysis**: novel methods, evaluation strategies and clinical applications *(PID2023-146261OB-I00, 2024 to 2027)*. |
| - **Computational system for the diagnosis of acute leukemia and lymphoma** from peripheral blood images, including a proof of concept and technological valorization *(PDC2022-133514-I00, 2022 to 2024)*. |
| - **CellsiMaticDeep**, computational hematology: deep learning solutions for the diagnosis of hematological diseases from peripheral blood cell images *(PID2019-104087RB-I00, 2020 to 2023)*. |
| - **Characterization and automatic classification of leukemic cells** by means of digital image processing and pattern recognition for diagnosis support *(DPI2015-64493-R, 2016 to 2018)*. |
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| ## Institutions and collaborations |
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| - **Universitat Politècnica de Catalunya (UPC)** |
| - **Hospital Clínic de Barcelona**, CORE Laboratory |
| - **Hospital Sant Joan de Déu**, Barcelona |
| - **Centro de Diagnóstico Biomédico (CDB)** |
| - International partners across Europe, Latin America and Asia |
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| ## Team |
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| - **Dr. José Rodellar.** Professor (Emeritus), UPC. Signal processing and machine learning for blood cell morphology. [Profile](https://futur.upc.edu/178882) |
| - **Santiago Alférez.** Assistant Professor, Dept. of Mathematics, UPC. Machine learning, statistics and explainable AI for medical image analysis. [Profile](https://futur.upc.edu/EdwinSantiagoAlferezBaquero) |
| - **Kevin Barrera.** Assistant Professor, UPC. Deep learning for cell morphology. [Profile](https://futur.upc.edu/KevinIvanBarreraLlanga) |
| - **Dr. Anna Merino.** Clinical lead, CORE Laboratory, Hospital Clínic de Barcelona. Hematology and cell morphology. |
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| ## Datasets and Models |
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| Our public datasets and models will be published here soon. Stay tuned. |
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| ## Links |
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| - Website: [cellsilab.com](https://cellsilab.com/) |
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| *For collaborations or inquiries, please reach out through our [website](https://cellsilab.com/).* |
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