Deep Learning

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 …

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 …

Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart. The literature in this field of research …

Liver lesion localisation and classification with convolutional neural networks: a comparison between conventional and spectral computed tomography

Invited Talk: Towards Deep Federated Learning in Healthcare

Keynote Speaker: Towards Deep Federated Learning in Healthcare

Organizing Committee Member at MICCAI DART 2019