Deep Learning

Invited Talk at 3MT Competition at Beirut Arab University

Today I was privileged to give a keynote speech at the Beirut Arab University in Lebanon 🇱🇧. My talk was about the impact of AI in the field of Medicine and how AI is changing the way healthcare professionals diagnose, treat, and care for hashtag#patients. My presentation included a couple of examples from our hashtag#research on microscopic imaging, but I also looked at a couple of hashtag#startups that are taking this to the next level by transforming healthcare through their hashtag#innovative products. Having a fruitful discussion with the audience was a real pleasure for me! Thanks so much, Dr. Nada El Darra and Dr. Said El Shamieh, for the warm welcome and the invitation 🙏

Utilising Artificial Intelligence in Cancer Imaging To Improve Patient Outcomes

Bonn-Melbourne Research Excellence Fund (2023-2024)

MA Thesis: Deep Learning based model for detection and grading of prostate cancer using mpMRI and MR-Fingerprinting -- Not available

Abstract. Prostate cancer (PCa) is the most common cancer in men and the second leading cause of cancer death in Germany [4,14]. Both digital rectal examination (DRE) along with the prostate-specific antigen (PSA) level in blood samples are typically used in PCa screening.

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings

Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-silo FL setting corresponds to the case of few (--) reliable clients, …

Course: Introduction to Machine Learning

Machine Learning has gained a lot of momentum within development organizations that are actively looking for innovative solutions to leverage their data to identify new levels of understanding their operations and processes. Machine learning is a subfield of Artificial Intelligence where the machine learns from data rather than from explicit programming.

Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the …

Invited Talk at the Workshop on Collaborative Learning: From Theory to Practice

I had the pleasure to give an invited talk at the #Collaborative Learning workshop at MBZUAI (Mohamed bin Zayed University of Artificial Intelligence)! It was a wonderful weekend full of amazing talks and fruitful discussions! I had the pleasure to meet a few familiar faces in our community along with other great speakers from UC Berkeley, Harvard, MIT, KAUST, ETH Zurich, Nvidia, and EPFL, among others. I would like to thank Michael I. Jordan and the organizing team behind the workshop for the invitation and the excellent hospitality! For those who are interested in the talks, they will be made publicly available soon!

Federated disentangled representation learning for unsupervised brain anomaly detection

With the advent of deep learning and increasing use of brain MRIs, a great amount of interest has arisen in automated anomaly segmentation to improve clinical workflows; however, it is time-consuming and expensive to curate medical imaging. Moreover, …

Development of a Deep Learning Toolkit for MRI-Guided Online Adaptive Radiotherapy

The 3-year DFG Funded project with LMU and TU Munich (2022-2025)

Invited Talk at AI 4 Imaging

I will deliver a talk in Federated Deep Learning in Healthcare