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, …

Federated Learning

Our recent algorithms in FL with Medical Imaging

Organizing a workshop on the Next Generation of AI in Medicine

BigPicture Project

The 6-year, €70 million project called BIGPICTURE will herald a new era in pathology

Federated Disentangled Representation Learning for Unsupervised Brain Anomaly Detection

Recent advances in Deep Learning (DL) and the increased use of brain MRI have provided a great opportunity and interest in automated anomaly segmentation to support human interpretation and improve clinical workflow. However, medical imaging must be …

FedPerl: Semi-Supervised Peer Learning for Skin Lesion Classification

Skin cancer is one of the most deadly cancers worldwide. Yet, it can be reduced by early detection. Recent deep-learning methods have shown a dermatologist-level performance in skin cancer classification. Yet, this success demands a large amount of …

The Future of Digital Health with Federated Learning

Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by …

Deep Federated Learning in Healthcare

One of our recent and promising projects.

Organizing Committee Member at MICCAI DCL 2020