research
∙
09/05/2020
Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling
Automatic segmentation of multi-sequence (multi-modal) cardiac MR (CMR) ...
share
research
∙
09/05/2020
Semi-supervised Pathology Segmentation with Disentangled Representations
Automated pathology segmentation remains a valuable diagnostic tool in c...
research
∙
08/27/2020
Metrics for Exposing the Biases of Content-Style Disentanglement
Recent state-of-the-art semi- and un-supervised solutions for challengin...
research
∙
08/26/2020
Disentangled Representations for Domain-generalized Cardiac Segmentation
Robust cardiac image segmentation is still an open challenge due to the ...
research
∙
12/04/2019
Learning to synthesise the ageing brain without longitudinal data
Brain ageing is a continuous process that is affected by many factors in...
research
∙
11/11/2019
Disentangle, align and fuse for multimodal and zero-shot image segmentation
Magnetic resonance (MR) protocols rely on several sequences to properly ...
research
∙
08/29/2019
Temporal Consistency Objectives Regularize the Learning of Disentangled Representations
There has been an increasing focus in learning interpretable feature rep...
research
∙
07/11/2019
FIRE: Unsupervised bi-directional inter-modality registration using deep networks
Inter-modality image registration is an critical preprocessing step for ...
research
∙
03/22/2019
Factorised Representation Learning in Cardiac Image Analysis
Typically, a medical image offers spatial information on the anatomy (an...
research
∙
01/10/2019
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization
Pseudo healthy synthesis, i.e. the creation of a subject-specific `healt...
research
∙
03/19/2018