30 citations · 116 across the 24 of their papers we have counts for
11 papers · 1 filter
Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation
Giammarco La Barbera, Pietro Gori, Haithem Boussaid +6
Due to a high heterogeneity in pose and size and to a limited number of available data, segmentation of pediatric images is challenging for deep learning methods. In this work, we…
Knowledge distillation from multi-modal to mono-modal segmentation networks
Minhao Hu, Matthis Maillard, Ya Zhang +4
The joint use of multiple imaging modalities for medical image segmentation has been widely studied in recent years. The fusion of information from different modalities has demonst…
Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example
Robin Kips, Ruowei Jiang, Sileye Ba +5
While makeup virtual-try-on is now widespread, parametrizing a computer graphics rendering engine for synthesizing images of a given cosmetics product remains a challenging task. I…
Approximation of dilation-based spatial relations to add structural constraints in neural networks
Mateus Riva, Pietro Gori, Florian Yger +2
Spatial relations between objects in an image have proved useful for structural object recognition. Structural constraints can act as regularization in neural network training, imp…
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification
Gianni Franchi, Andrei Bursuc, Emanuel Aldea +2
Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks. Whi…
Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations
Antoine Pirovano, Hippolyte Heuberger, Sylvain Berlemont +2
Deep learning methods are widely used for medical applications to assist medical doctors in their daily routines. While performances reach expert's level, interpretability (highlig…