Deep Learning

Modelling Uncertainty in Deep Learning for Medical Applications

DAAD Funded Project with ETH Zürich and Imperial College London (2020-2022)

Uncertainty Aware Methods for Camera Pose Estimation and Relocalization

BaCaTeC Funded Project with Stanford University and Siemens AG (2020-2021)

Organizing Committee Member at MICCAI DART 2020

Organizing Committee Member at MICCAI DCL 2020

Uncertainty-based graph convolutional networks for organ segmentation refinement

Organ segmentation is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, CNNs have dominated the state of the art in this task. Organ segmentation scenarios present a challenging …

Invited Talk: Towards Deep Federated Learning in Healthcare

Modelling Labels Uncertainty in Medical Imaging

Keynote Speaker: AI in Healthcare

A learning without forgetting approach to incorporate artifact knowledge in polyp localization tasks

Colorectal polyps are abnormalities in the colon tissue that can develop into colorectal cancer. The survival rate for patients is higher when the disease is detected at an early stage and polyps can be removed before they develop into malignant …

An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy

We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art …