Biomedical Imaging

In addition to the projects highlighted below, we are part of the project PET++ on improving localisation, diagnosis and quantification in clinical and medical PET imaging with randomised optimisation. This project includes collaborators from the University of Bath, the University of Oxford, University College London, KTH Stockholm, Kings College London, the University of Helsinki, and Addenbrookes' Hospital.

Cellular Mechanics of Drosophila

Researcher: Lukas F. Lang, Jocelyn Étienne, Nilankur Dutta, Bénédicte Sanson, Elena Scarpa, Carola-Bibiane Schönlieb

Faster PET Reconstruction by Stochastic Optimisation

Researcher: Matthias Ehrhardt, and Carola-Bibiane Schönlieb

Flow of Microtubules in the Drosophila Oocyte

Researcher: Lukas F. Lang, Maik Drechsler, Hendrik Dirks, Martin Burger, Carola-Bibiane Schönlieb, Isabel M. Palacios

Image Classification Under Minimal Supervision: Graph-Based Semi-Supervised Learning for Real-World Large-Scale Problems.

Reseacher: Angelica Aviles-Rivero, and Carola-Bibiane Schönlieb

Inverse Problems with Imperfect Forward Models and Applications in Biomedical Imaging

Researcher: Yury Korolev, Martin Burger, Carola-Bibiane Schönlieb, Leila Muresan

Learning Sampling Patterns for MRI

Researcher: Ferdia Sherry, Erlend Riis, Luca Calatroni, and Carola-Bibiane Schönlieb

Mathematical challenges in electron tomography

Researchers: Willem Diepeveen, Rob Tovey, Tatiana Bubba, M. Benning, C.-B. Schönlieb, O. Öktem, C.E. Yarman

Multi-Tasking Models for Image Analysis

Researcher: Angelica Aviles Rivero, Veronica Corona, Noémie Debroux, Carola-Bibiane Schönlieb

Template-Based Image Reconstruction from Sparse Tomographic Data

Researcher: Lukas F. Lang, Sebastian Neumayer, Ozan Öktem, Carola-Bibiane Schönlieb

Tracking Tumour Dynamics

Researchers: Thomas Buddenkotte, Subhadip Mukherjee, Carola-Bibiane Schönlieb and Evis Sala

Unsupervised Learning for Image Analysis

Researcher: Angelica Aviles-Rivero, Hankui Peng, Lihao Liu, and Carola-Bibiane Schönlieb

Machine learning for brain and mental health

Researcher: Zhongying Deng

This project focuses on the development and implementation of state-of-the-art machine learning and image analysis techniques for the early diagnosis of mental health disorders (e.g. dementia, mood-related disorders). The research aims to develop biologically-inspired artificial systems for precision brain and mental health by bringing together expertise in machine learning, data science, neuroscience, and clinical practice.

Translational research on Computed Tomography

Researcher: Ander Biguri

Federated Learning for Healthcare

Researcher: Fan Zhang

LION: tools for data driven CT reconstruction