Daad |

Symposium on AI in Medicine & Food Science

Mon, 01 Dec 2025 00:00:00 +0000

Time: All times correspond to local Bonn time (CET)

Day 1 — Tuesday, 9 December 2025

Arrival & Check-in
📍 Youth Hostel Bonn, Haager Weg 42, 53127 Bonn

Day 2 — Wednesday, 10 December 2025

Symposium on AI in Medicine
📍 Baunscheidtstraße 17, 53113 Bonn

Time	Session	Speaker
09:30–10:00	Registration	—
10:00–10:30	Welcome Speech
10:30–11:45	Keynote Speech: Seeing, Sensing, Securing: AI in Modern Medicine
11:45–12:00	Therapeutic & Surgical Simulation in Medicine
12:00–12:15	AI-Driven Segmentation for Early Eye Disease Detection
12:15–12:30	“Ocean modeling in the Eastern Mediterranean: insights from physical based modelling, data assimilation and machine learning.
12:30–14:00	Lunch Break	—
14:00–14:15	FIDAL: Diversity-Aware Federated Active Learning Under Real-World Distribution Shifts
14:15–14:30	MRIQT: Physics-Aware Diffusion Model for Image Quality Transfer in Neonatal Ultra-Low-Field MRI
14:30–14:45	Rapid and affordable detection of Giant Cell Arteritis using B-mode ultrasound
14:45–15:15	Break	—
15:15–15:30	Teleradiology System Integrating DICOMweb and AI	Molka Yermani
15:30–15:45	Lightweight Bayesian Deep Learning for Point-of-Care Breast Ultrasound	Zeravan Ali
15:45–16:00	Modality‑Aware Deep Learning for Human Bone Fracture Recognition on HBFMID	Omar Al-Zakholi
16:00–16:15	Embedding BI-RADS Foundational Concepts for Cross-Domain and Cross-Modality Generalization in Breast Imaging
16:15–16:30	ML for Histopathological Classification of Conjunctival Melanocytic Intraepithelial Lesion
16:30–16:45	Affordable AI for Retinal Disease Detection
16:45–17:00	Wrap-up Discussion

Day 3 — Thursday, 11 December 2025

Symposium on Food Science
📍 Käthe-Kümmel-Str. 1, 53115 Bonn

Time	Session	Speaker
14:25–14:30	Welcome Speech	&
14:30–15:00	Proteins Under the Microscope: Extraction, Modification and Characterisation
15:00–15:10	Effects of processing on protein-bound amino acids
15:10–15:30	Innovative plant protein sources: Common bean and hempseed press cake as raw material for protein ingredient production	&
15:30–16:00	Break	—
16:00–16:15	From Questions to Discoveries: Highlights from My Food Science Research Career
16:15–16:30	Driving Excellence in Nutrition and Food Science: LAU’s NFS Department’s Mission and Work
16:30–16:40	Micro and Nanoplastics in table salt: occurence and exposure	Hanin El Hajj
16:40–16:50	Unmasking gluten contamination in gluten-free products	Roa Echtay
16:50–17:10	Wrap-up Discussion	Shadi & Ismail
17:10–17:30	Lab Tour

We have the symposium a bit late as one of the key guest’s expertise in Food Science will only arrive in the afternoon

Day 4 — Friday, 12 December 2025

Roundtable discussion in our lab + Excursion Day (Self-Organized)** 📍 Baunscheidtstraße 17, 53113 Bonn

For the Excurssion, here are some suggested visits: Bonn Christmas Market, Cologne Cathedral, Chocolate Museum Köln.
📍 Bonn / Cologne region

Day 5 — Saturday, 13 December 2025

Departure Day
📍 Youth Hostel Bonn

Summer School on Biomedical Imaging with Deep Learning (BILD)

Sun, 01 Jun 2025 00:00:00 +0000

Strategic Arab-German Network for Affordable and Democratized AI (SANAD)

Wed, 01 Jan 2025 00:00:00 +0000

In recent years, the transformative potential of Artificial Intelligence (AI) in revolutionizing global healthcare has become evident, offering promising advancements in diagnostic precision, personalized treatment, and healthcare delivery. However, integrating AI into low-resource environments, especially in low- and middle-income countries (LMICs), remains constrained by significant barriers such as limited computational resources, inadequate local data, and financial constraints. The Strategic Arab-German Network for Affordable and Democratized AI in Healthcare (SANAD- سند) seeks to address these challenges by fostering collaboration, empowering local expertise, and advancing Affordable AI and Collaborative Federated Learning solutions tailored to underserved regions. The word “SANAD” means support in Arabic, which is what this project is about.

SANAD aims to bridge the healthcare gap between resource-rich and resource-limited settings, particularly the Global North and Global South disparity. Many existing AI models are developed using centralized datasets from well-funded institutions, which often fail to generalize across diverse populations and contexts. SANAD proposes a strategic, inclusive approach to democratizing access to AI by leveraging interdisciplinary expertise and promoting knowledge-sharing between Arab and German stakeholders

Measures:

, Tunisia
, Germany

Affordable AI and Collaborative Federated Learning for Global Healthcare (EEDA)

Fri, 01 Sep 2023 00:00:00 +0000

Modelling Uncertainty in Deep Learning for Medical Applications

Wed, 01 Jan 2020 00:00:00 +0000

Deep Learning has emerged as a leading technology for accomplishing many challenging tasks showing outstanding performance in a broad range of applications in computer vision and medical applications. Despite its success and merit in recent state-of-the-art methods, DL tools still lack in robustness hindering its adoption in medical applications. Modeling uncertainty, through Bayesian Inference and Monte-Carlo dropout, has been successfully introduced to computer vision for better understanding the underlying deep learning models. In this proposal, we investigate modeling the uncertainty for medical applications given the well-known challenges in medical image analysis, namely severe class-imbalance, few amounts of labeled data, domain shift, and noisy annotations.

Collaboration:

Prof. , , .

Prof. , ,