Initial commit: CellsiLAB organization card

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README.md

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+---
+title: CellsiLAB
+emoji: 🔬
+colorFrom: purple
+colorTo: pink
+sdk: static
+pinned: false
+---
+
+# CellsiLAB: Cells and Image Analysis
+
+**CellsiLAB** is the biomedical image analysis area of the **Codalab** research group at the **Universitat Politècnica de Catalunya (UPC)**.
+
+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*.
+
+## Research lines
+
+- **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.
+
+## Application domains
+
+- **Hematology**: blood cell classification, myelodysplastic syndromes, leukemia and red cell morphology.
+- **Obstetrics**: fetal ultrasound quality assessment (nuchal translucency).
+- **Pediatric ophthalmology**: retinopathy of prematurity.
+
+## Research projects
+
+- **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)*.
+
+## Institutions and collaborations
+
+- **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
+
+## Team
+
+- **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.
+
+## Datasets and Models
+
+Our public datasets and models will be published here soon. Stay tuned.
+
+## Links
+
+- Website: [cellsilab.com](https://cellsilab.com/)
+
+---
+
+*For collaborations or inquiries, please reach out through our [website](https://cellsilab.com/).*

index.html

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+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1" />
+  <title>CellsiLAB: Cells and Image Analysis</title>
+  <style>
+    :root { color-scheme: light dark; }
+    body {
+      margin: 0;
+      font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
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+      color: #e5e7eb;
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+    h1 { font-size: 1.7rem; margin: 0 0 .2rem; color: #fff; }
+    h2 {
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+    }
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+    a:hover { text-decoration: underline; }
+    ul { margin: .4rem 0; padding-left: 1.2rem; }
+    li { margin: .25rem 0; }
+    .tagline { color: #9ca3af; font-size: .95rem; margin-bottom: 1rem; }
+    code { background: rgba(255,255,255,.08); padding: .1rem .35rem; border-radius: 4px; font-size: .85em; }
+  </style>
+</head>
+<body>
+  <div class="wrap">
+    <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>
+  </div>
+</body>
+</html>