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.
I am deeply grateful to the organizers of this symposium for bringing together Syrian and Arab scholars in such a meaningful way. It is a privilege to have the opportunity to present on the topic of collective intelligence, highlighting the importance of interdisciplinary research in shaping a better future. Events like these foster collaboration and inspire innovative solutions that transcend boundaries, and I am honored to be a part of it.
I'm truly honored and excited to be invited to participate in the Helmholtz Annual Meeting 2024. I was particularly inspired by the insightful talk on synaptic transmission by Nobel laureate and Stanford Professor Thomas C. Südhof. His groundbreaking work continues to shape our understanding of neuroscience and opens new doors for innovation in the field. Additionally, the panel discussion led by BMBF Minister Ms. Bettina Stark-Watzinger was highly thought-provoking, especially her emphasis on fostering research and development in Germany. These sessions highlight the critical role of science and policy in shaping our future.
I was fortunate enough to attend and participate at the DAAD PRIME Alumni Seminar where I had the chance to meet and re-connect with PRIME fellows and a couple of colleagues! We exchanged career paths and trajectories and heard several success stories in both academia and industries.
It is a pleasure to accept the invitation to deliver a talk at the The Artificial Intelligence and Machine-Learning (AIMS) working group of the North American Imaging in MS (NAIMS) Cooperative. I'll present our Nature Machine Intelligence Paper on anomaly detection in MR Brain Imaging which includes MS Lesions.
Abstract. Conjunctival Melanocytic Intraepithelial Lesions (CMIL) are a significant precursor to conjunctival melanoma, a rare but potentially fatal ocular cancer. The histopathological classification of CMIL is crucial for early diagnosis and treatment planning.
We are excited to announce that Albarqouni Lab is proud to be one of the host institutions for the prestigious DAAD AInet Fellowship, which is awarded twice a year to outstanding international early-career researchers in artificial intelligence. This fellowship offers a unique opportunity to join the Postdoctoral Networking Tour in Artificial Intelligence (Postdoc-NeT-AI), where awardees can engage with leading researchers in Germany, fostering collaborations and creating new research and career opportunities. At Albarqouni Lab, we are at the forefront of AI research in medical imaging, machine learning, and large language models, and have successfully hosted several AInet Fellows in the past. We invite interested applicants to apply and consider joining us to advance cutting-edge AI research in healthcare. More details can be found [here](https://www.daad.de/en/the-daad/postdocnet/details-and-application/).
Abstract. Breast density is a critical factor in breast cancer risk and detection, influencing the effectiveness of mammography. Higher breast density, characterized by a greater proportion of fibroglandular tissue relative to fatty tissue, is associated with a four- to sixfold increase in breast cancer risk.