David presented our research at MICAD 2024 in Manchester, UK, titled *Deep Learning for Lymph Node Metastasis Detection in Pancreatic Ductal Adenocarcinoma.* It was a rewarding experience to share our work with such an engaged and dynamic community.
It was a pleasure moderating the Symposium on AI in Medicine and sharing our recent works, which have recently been accepted at hashtag#MICAD and hashtag#MICCAI Workshops! Thanks to the team members Sarah Schaab, David D. Gaviria, and Elyes Farjallah for their great efforts! Thanks to the co-moderator Prof. Valentin S. Schäfer, and the organizing team behind the MIB Future Panel; Lisa Mona Marie Senner, Prof. Frank G. Holz, and the dean, Prof. Bernd Weber, among others! Congrats to the winners!
It was an exciting to present our research on *Lymph Node Metastasis Detection in Pancreatic Ductal Adenocarcinoma* at the MIB Future Panel 2024. Hosted by the *Medical Imaging Center Bonn* in partnership with *Universitätsklinikum Bonn* and the Medical Faculty of *Rheinische Friedrich-Wilhelms-Universität Bonn*, this symposium united thought leaders from both academia and industry.
David participated in the *Student Retreat 2024* organized by the BIGS Clinical and Population Science PhD program in Bonn, Germany. This event was an invaluable opportunity to present my research in a poster format, engage with fellow scholars, and exchange scientific knowledge.
Abstract: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, with lymph node metastasis (LNM) being a critical determinant in patient prognosis and therapeutic planning [1-2]. Conventional methods for detecting LNM in PDAC primarily rely on contrast-enhanced CT scans, but these often fall short in sensitivity, especially in early-stage disease.
I had the pleasure to give a talk at the DESAM Congress at the Near East University in Nicosia. I had the chance to meet and discuss further collaboration with [rectorate](https://neu.edu.tr/about-us/management/rectorate/?lang=en) reprsented by the rector, Prof. Dr. Tamer ŞANLIDAĞ, vice rector; Prof. Dr. Umut AKSOY, and his advisors; Prof. Dr. Murat SAYAN and Assoc. Prof. Dr. Dilber UZUN ÖZŞAHİN at the Near East University.
At the World Health Summit, I had the opportunity to participate in key sessions moderated by GLOHRA, such as *Stronger Together: Improving Research Partnerships with LMICs,* where we heard from influential figures, including parliament members, and sessions like *Bridging Policy and Research: Translating the EU Global Health Strategy into Action* and *Building Success: On the Road to the 'Nutrition for Growth'.*
Despite the difficult circumstances, we are proud to contribute to MICCAI 2024 with a few papers besides being on board the organizing committee of MICCAI 2024 and DeCaF Workshop! looking forward to meeting and interacting with #young #talents at #MICCAI in #Marrakech next week.
Join us in our #GLOHRA session on Federated Learning with Medical Imaging! This is quite an important topic nowadays to tackle the data privacy issue while leveraging the collective intelligence we have in multi-national and diverse medical institutes around the globe! It is the way forward to bridge the gap between the global north and global south in medicine and healthcare! This aligns with our recent activities in organizing the [5th workshop on Collaborative and Federated Learning](https://decaf-workshop.github.io/decaf-2024/) which will happen in Morocco in conjunction with MICCAI, and our recent [IEEE-TMI Special Issue on Federated Learning](https://ieeexplore.ieee.org/document/10169005). I am looking forward to meeting you on 1st October!
The Computational Imaging Research (Albarqouni Lab) at the Clinic of Diagnostic and Interventional Radiology at the University Hospital Bonn is pleased to present a seminar on *Artificial Intelligence in Radiology: Applications and Research.* This cutting-edge seminar, spanning two semester hours per week (2 SWS), is specially curated for medical students to explore the transformative role of deep learning in medical imaging, with a focus on tasks such as classification, detection, segmentation, reconstruction, tracking, and disease progression.