Medical Imaging

Learn to Adapt

Domain Adaptation, Style Transfer

Learn to Learn

Meta-Learning, Few-Shot Learning

Learn to Reason and Explain

Interpretable ML, Disentangled Representation, Fairness

Learn to Recognize

Detection, Classification, Segmentation, Anomaly Detection, Semi-/Weakly-Supervised Learning

Modelling Uncertainty in Deep Learning for Medical Applications

DAAD PRIME Fellowship at ETH Zürich and Imperial College London

Telemedicine in Palestine

Telemedicine in Palestine from Shadi Nabil Albarqouni Collaboration: Funding:

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 …

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 …

Benefit of dual energy CT for lesion localization and classification with convolutional neural networks

Dual Energy CT is a modern imaging technique that is utilized in clinical practice to acquire spectral information for various diagnostic purposes including the identification, classification, and characterization of different liver lesions. It …