23 citations · 37 across the 13 of their papers we have counts for
8 papers · 1 filter
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
Carole H. Sudre, Kimberlin Van Wijnen, Florian Dubost +46
Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and…
Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration
Bo Li, Wiro J. Niessen, Stefan Klein +4
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this me…
Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI
Bo Li, Wiro J. Niessen, Stefan Klein +3
Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysi…
Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks
Florian Dubost, Hieab Adams, Pinar Yilmaz +6
Finding automatically multiple lesions in large images is a common problem in medical image analysis. Solving this problem can be challenging if, during optimization, the automated…
Hydranet: Data Augmentation for Regression Neural Networks
Florian Dubost, Gerda Bortsova, Hieab Adams +4
Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annot…
3D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRI
Florian Dubost, Hieab Adams, Gerda Bortsova +4
Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various ne…
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
Florian Dubost, Gerda Bortsova, Hieab Adams +4
We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a…
Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners
Veronika Cheplygina, Annegreet van Opbroek, M. Arfan Ikram +2
Supervised learning has been very successful for automatic segmentation of images from a single scanner. However, several papers report deteriorated performances when using classif…