352 citations · 784 across the 12 of their papers we have counts for
12 papers
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…
Image Compositing for Segmentation of Surgical Tools without Manual Annotations
Luis C. Garcia-Peraza-Herrera, Lucas Fidon, Claudia D'Ettorre +3
Producing manual, pixel-accurate, image segmentation labels is tedious and time-consuming. This is often a rate-limiting factor when large amounts of labeled images are required, s…
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.…
Distributionally Robust Deep Learning using Hardness Weighted Sampling
Lucas Fidon, Michael Aertsen, Thomas Deprest +14
Limiting failures of machine learning systems is of paramount importance for safety-critical applications. In order to improve the robustness of machine learning systems, Distribut…
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…
Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks
Lucas Fidon, Wenqi Li, Luis C. Garcia-Peraza-Herrera +4
The Dice score is widely used for binary segmentation due to its robustness to class imbalance. Soft generalisations of the Dice score allow it to be used as a loss function for tr…
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
Wenqi Li, Guotai Wang, Lucas Fidon +3
Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing efficient deep architectures to analyse volumetric medical…
ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools
Luis C. Garcia-Peraza-Herrera, Wenqi Li, Lucas Fidon +8
Real-time tool segmentation from endoscopic videos is an essential part of many computer-assisted robotic surgical systems and of critical importance in robotic surgical data scien…
Scalable multimodal convolutional networks for brain tumour segmentation
Lucas Fidon, Wenqi Li, Luis C. Garcia-Peraza-Herrera +4
Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these mod…