About me
Hello and welcome to my website! My name is Felix Wagner, and I’m a PhD student at the Engineering Department of the University of Oxford supervised by Professor Konstantinos Kamnitsas. My research focuses on Computer Vision for Medical Imaging, with a specific emphasis on Federated Learning and Domain Adaptation. I am particularly interested in addressing distribution shifts in heterogeneous decentralized datasets during collaborative model training. In real-world medical applications, it is crucial to develop methods that enable AI models to adapt and perform well in new, unseen environments. In general, my interest lies in enhancing the reliability of AI models in the medical sector
Before starting my PhD journey, I earned both my bachelor’s and master’s degrees at TU Wien in Computer Science, where I focused on combining symbolic and subsymbolic AI.
News
[10/24] Exciting news! My paper Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities 🧠, was accepted at WACV 2025. Looking forward to presenting our results in Arizona! 🤠
[10/24] Received an honorable mention as outstanding reviewer for the MICCAI conference 📚. I hope my reviews contributed to the community and help improve future submissions.
[05/24] The paper Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation, led by my colleague Wentian Xu, in which I am co-author, was accepted at MIDL 2024 and selected for an oral presentation.
Selected Publications
Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities
Felix Wagner, Wentian Xu, Pramit Saha, Ziyun Liang, Daniel Whitehouse, David Menon, Virginia Newcombe, Natalia Voets, J. Alison Noble, Konstantinos Kamnitsas
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025
Traditionally, brain lesion MRI segmentation models are trained for a specific disease, with a predefined set of input modalities. These models are usually not able to process MRI scans with different sets of input modalities. In this paper, we introduce FedUniBrain, which demonstrates for the first time that, in a decentralized setting without any data exchange, a single model can be trained to segment multiple types of brain lesions from databases with different sets of MRI modalities.
Post-Deployment Adaptation with Access to Source Data via Federated Learning and Source-Target Remote Gradient Alignment
Felix Wagner, Zeju Li, Pramit Saha, Konstantinos Kamnitsas
Machine Learning in Medical Imaging (MLMI 2023) workshop at MICCAI 2023
Usually deployed models cannot adapt reliably because we often do not have access to the training data anymore because of privacy conerns. In this paper we enable deployed models to access training data via federated learning without data exchange. We propose an algorithm to align gradients of source and target data to adapt deployed models to a target distribution.