352 citations · 784 across the 12 of their papers we have counts for
6 papers · 1 filter
Trustworthy Deep Learning for Medical Image Segmentation
Lucas Fidon
Despite the recent success of deep learning methods at achieving new state-of-the-art accuracy for medical image segmentation, some major limitations are still restricting their de…
MONAIfbs: MONAI-based fetal brain MRI deep learning segmentation
Marta B. M. Ranzini, Lucas Fidon, Sébastien Ourselin +2
In fetal Magnetic Resonance Imaging, Super Resolution Reconstruction (SRR) algorithms are becoming popular tools to obtain high-resolution 3D volume reconstructions from low-resolu…
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Abel Díaz Berenguer, Hichem Sahli, Boris Joukovsky +37
Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised c…
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge
Lucas Fidon, Sebastien Ourselin, Tom Vercauteren
Training a deep neural network is an optimization problem with four main ingredients: the design of the deep neural network, the per-sample loss function, the population loss funct…
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
Sofie Tilborghs, Ine Dirks, Lucas Fidon +19
Recent research on COVID-19 suggests that CT imaging provides useful information to assess disease progression and assist diagnosis, in addition to help understanding the disease.…
Incompressible image registration using divergence-conforming B-splines
Lucas Fidon, Michael Ebner, Luis C. Garcia-Peraza-Herrera +3
Anatomically plausible image registration often requires volumetric preservation. Previous approaches to incompressible image registration have exploited relaxed constraints, ad ho…