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 <h3>About us</h3>
-At CCIBonn.ai (Divison for Computational Radiology & Clinical AI), we focus on the development and application of artificial intelligence in medical imaging to enhance diagnostic accuracy, optimize clinical workflows, and enable large-scale data-driven research. Led by Prof. Dr. Philipp Vollmuth, Else Kröner CS Professor for AI in Medical Imaging (Medical Faculty at the University of Bonn), our multidisciplinary team combines expertise from AI researchers, data scientists and clinicians to bridge the gap between computational research and clinical application.  
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-Our research includes foundational AI models for radiology, privacy-preserving federated learning, the use of synthetic data to overcome limitations in medical datasets, and the integration of large language models into healthcare workflows. With publications in journals such as Lancet Oncology, Nature Communications, and Lancet Digital Health, we are committed to translating computational research into clinically impactful solutions for precision medicine and radiology. We operate state-of-the-art infrastructure, including NVIDIA DGX H200 servers and have access to cutting-edge imaging systems such as 0.064T, 3T and 7T MRI scanners. We are key contributors to the Human Radiome Project, the MICCAI Brain Tumor Segmentation (BraTS) challenge and the Federated Tumor Segmentation (FeTS) initiative. Moreover, collaborations with leading institutions including the German Cancer Research Center (DFKZ), the German Center for Neurodegenerative Diseases (DZNE), and the European Organization for Research and Treatment of Cancer (EORTC) ensure the clinical and methodological rigor of our work.
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-<h3>Open Science</h3>
-We prioritize open science and share our research through our GitHub organizations github.com/CCI-Bonn and github.com/openradx. Notable repositories include HD-BET and HD-GLIO, which have become widely used tools in medical image processing. Additionally, our team is developing ADIT (Automated DICOM Transfer) and RADIS (Radiology Report Archive and Discovery System), which are powerful tools for facilitating data management and analysis in radiology. ADIT provides a versatile solution for exchanging DICOM data across systems through a user-friendly web interface, while RADIS supports the archiving, querying, and discovery of radiology reports, enabling efficient workflows and large-scale data mining. 
+At CCIBonn.ai (Divison for Computational Radiology & Clinical AI), we focus on the development and application of artificial intelligence in medical imaging to enhance diagnostic accuracy, optimize clinical workflows, and enable large-scale data-driven research. Led by Prof. Dr. Philipp Vollmuth, Else Kröner CS Professor for AI in Medical Imaging (Medical Faculty at the University of Bonn), our multidisciplinary team combines expertise from AI researchers, data scientists and clinicians to bridge the gap between computational research and clinical application. Our research includes foundational AI models for radiology, privacy-preserving federated learning, the use of synthetic data to overcome limitations in medical datasets, and the integration of large language models into healthcare workflows. We operate state-of-the-art infrastructure, including NVIDIA DGX H200 servers and have access to cutting-edge imaging systems such as 0.064T, 3T and 7T MRI scanners. We are key contributors to the Human Radiome Project, the MICCAI Brain Tumor Segmentation (BraTS) challenge and the Federated Tumor Segmentation (FeTS) initiative. Moreover, collaborations with leading institutions including the German Cancer Research Center (DFKZ), the German Center for Neurodegenerative Diseases (DZNE), and the European Organization for Research and Treatment of Cancer (EORTC) ensure the clinical and methodological rigor of our work.
 
 <h3>Affiliations</h3>
 CCIBonn.ai (Division for Computational Radiology & Clinical AI) is affiliated with the Faculty of Medicine at the University of Bonn, Germany. Philipp holds additional appointments at the Center for Medical Data Use and Translation (ZMDT), University of Bonn, and the Division for Medical Image Computing (MIC) at the German Cancer Research Center (DKFZ) in Heidelberg.
 
 <h3>Funding</h3>
 We have secured >4.5 million EUR in funding and we are currently supported by the European Research Counccil (ERC Consolidator Grant for "AI-Next"), the German Research Foundation (DFG - Priority Program 2177: Radiomics - Next Generation of Biomedical Imaging), BONFOR, and the Else Kröner Fresenius Foundation among others. 
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+<h3>Publications</h3>
+With publications in journals such as Lancet Oncology, Nature Communications, and Lancet Digital Health, we are committed to translating computational research into clinically impactful solutions for precision medicine and radiology. Full list of publications is available <a href="https://scholar.google.de/citations?user=z0ENYEQAAAAJ&hl=de">here</a>
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+<h3>Open Science</h3>
+We prioritize open science and share our research through our GitHub organizations github.com/CCI-Bonn and github.com/openradx. Notable repositories include HD-BET and HD-GLIO, which have become widely used tools in medical image processing. Additionally, our team is developing ADIT (Automated DICOM Transfer) and RADIS (Radiology Report Archive and Discovery System), which are powerful tools for facilitating data management and analysis in radiology. ADIT provides a versatile solution for exchanging DICOM data across systems through a user-friendly web interface, while RADIS supports the archiving, querying, and discovery of radiology reports, enabling efficient workflows and large-scale data mining.