91 citations · 160 across the 9 of their papers we have counts for
7 papers · 1 filter
Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders
Agostina J Larrazabal, César Martínez, Ben Glocker +1
We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms.…
Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets
Nicolas Roulet, Diego Fernandez Slezak, Enzo Ferrante
Brain lesion and anatomy segmentation in magnetic resonance images are fundamental tasks in neuroimaging research and clinical practice. Given enough training data, convolutional n…
Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration
Enzo Ferrante, Puneet K. Dokania, Rafael Marini Silva +1
Deformable registration has been one of the pillars of biomedical image computing. Conventional approaches refer to the definition of a similarity criterion that, once endowed with…
Left ventricle quantification through spatio-temporal CNNs
Alejandro Debus, Enzo Ferrante
Cardiovascular diseases are among the leading causes of death globally. Cardiac left ventricle (LV) quantification is known to be one of the most important tasks for the identifica…
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Konstantinos Kamnitsas, Wenjia Bai, Enzo Ferrante +8
Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation. However, the v…
Deformable Registration through Learning of Context-Specific Metric Aggregation
Enzo Ferrante, Puneet K Dokania, Rafael Marini +1
We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conven…