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<title>CellsiLAB: Cells and Image Analysis</title>
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<h1>CellsiLAB: Cells and Image Analysis</h1>
<p class="tagline">Biomedical image analysis area of the <strong>Codalab</strong> research group at the Universitat Politècnica de Catalunya (UPC).</p>
<p>We work on <strong>applied artificial intelligence</strong>, focused mainly (though not exclusively)
on <strong>biomedical image analysis</strong>. We develop <strong>deep learning</strong> and
<strong>computer vision</strong> methods to support clinical decision-making, with a strong emphasis on
<strong>explainable</strong> and <strong>trustworthy AI</strong>: we care not only about <em>what</em>
a model predicts, but <em>why</em>.</p>
<h2>Research lines</h2>
<ul>
<li><strong>Explainable AI (xAI)</strong> for biomedical imaging: new methodologies, evaluation strategies and clinical applications.</li>
<li><strong>Computer vision for medical imaging</strong>: classification, segmentation and quality assessment.</li>
<li><strong>Generative and vision-language models</strong> applied to medical data.</li>
<li><strong>Federated learning</strong> for robust multi-center generalization.</li>
</ul>
<h2>Application domains</h2>
<ul>
<li><strong>Hematology</strong>: blood cell classification, myelodysplastic syndromes, leukemia and red cell morphology.</li>
<li><strong>Obstetrics</strong>: fetal ultrasound quality assessment (nuchal translucency).</li>
<li><strong>Pediatric ophthalmology</strong>: retinopathy of prematurity.</li>
</ul>
<h2>Research projects</h2>
<ul>
<li><strong>Explainable deep learning in medical image analysis</strong>: novel methods, evaluation strategies and clinical applications <em>(PID2023-146261OB-I00, 2024 to 2027)</em>.</li>
<li><strong>Computational system for the diagnosis of acute leukemia and lymphoma</strong> from peripheral blood images, including a proof of concept and technological valorization <em>(PDC2022-133514-I00, 2022 to 2024)</em>.</li>
<li><strong>CellsiMaticDeep</strong>, computational hematology: deep learning solutions for the diagnosis of hematological diseases from peripheral blood cell images <em>(PID2019-104087RB-I00, 2020 to 2023)</em>.</li>
<li><strong>Characterization and automatic classification of leukemic cells</strong> by means of digital image processing and pattern recognition for diagnosis support <em>(DPI2015-64493-R, 2016 to 2018)</em>.</li>
</ul>
<h2>Institutions and collaborations</h2>
<ul>
<li><strong>Universitat Politècnica de Catalunya (UPC)</strong></li>
<li><strong>Hospital Clínic de Barcelona</strong>, CORE Laboratory</li>
<li><strong>Hospital Sant Joan de Déu</strong>, Barcelona</li>
<li><strong>Centro de Diagnóstico Biomédico (CDB)</strong></li>
<li>International partners across Europe, Latin America and Asia</li>
</ul>
<h2>Team</h2>
<ul>
<li><strong>Dr. José Rodellar.</strong> Professor (Emeritus), UPC. Signal processing and machine learning for blood cell morphology. <a href="https://futur.upc.edu/178882" target="_blank">Profile</a></li>
<li><strong>Santiago Alférez.</strong> Assistant Professor, Dept. of Mathematics, UPC. Machine learning, statistics and explainable AI for medical image analysis. <a href="https://futur.upc.edu/EdwinSantiagoAlferezBaquero" target="_blank">Profile</a></li>
<li><strong>Kevin Barrera.</strong> Assistant Professor, UPC. Deep learning for cell morphology. <a href="https://futur.upc.edu/KevinIvanBarreraLlanga" target="_blank">Profile</a></li>
<li><strong>Dr. Anna Merino.</strong> Clinical lead, CORE Laboratory, Hospital Clínic de Barcelona. Hematology and cell morphology.</li>
</ul>
<h2>Datasets and Models</h2>
<p>Our public datasets and models will be published here soon. Stay tuned.</p>
<h2>Links</h2>
<ul>
<li>Website: <a href="https://cellsilab.com/" target="_blank">cellsilab.com</a></li>
</ul>
<p style="margin-top:1.5rem;color:#9ca3af;font-size:.9rem;">
For collaborations or inquiries, please reach out through our
<a href="https://cellsilab.com/" target="_blank">website</a>.
</p>
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