The performance of learning-based algorithms improves with the amount of...
Deep learning methods typically depend on the availability of labeled da...
Finite element methods (FEM) are popular approaches for simulation of so...
Learning similarity is a key aspect in medical image analysis, particula...
Supervised learning is well-known to fail at generalization under
distri...
Semi-supervised segmentation tackles the scarcity of annotations by
leve...
In recent years there has been a resurgence of interest in our community...
The physical and clinical constraints surrounding diffusion-weighted ima...
Domain adaptation (DA) has drawn high interest for its capacity to adapt...
Assessing the degree of disease severity in biomedical images is a task
...
Image normalization is a building block in medical image analysis.
Conve...
The segmentation of the retinal vasculature from eye fundus images repre...
Domain adaptation (DA) has drawn high interests for its capacity to adap...
Segmentation using deep learning has shown promising directions in medic...
Unpaired image-to-image translation has been applied successfully to nat...
The varying cortical geometry of the brain creates numerous challenges f...
Image normalization is a critical step in medical imaging. This step is ...
Brain surface analysis is essential to neuroscience, however, the comple...
The analysis of the brain surface modeled as a graph mesh is a challengi...
We propose to adapt segmentation networks with a constrained formulation...
Recently, dense connections have attracted substantial attention in comp...
Neuronal cell bodies mostly reside in the cerebral cortex. The study of ...