5 papers · 1 filter
Separating common from salient patterns with Contrastive Representation Learning
Robin Louiset, Edouard Duchesnay, Antoine Grigis +1
Contrastive Analysis is a sub-field of Representation Learning that aims at separating common factors of variation between two datasets, a background (i.e., healthy subjects) and a…
Double InfoGAN for Contrastive Analysis
Florence Carton, Robin Louiset, Pietro Gori
Contrastive Analysis (CA) deals with the discovery of what is common and what is distinctive of a target domain compared to a background one. This is of great interest in many appl…
Decoupled conditional contrastive learning with variable metadata for prostate lesion detection
Camille Ruppli, Pietro Gori, Roberto Ardon +1
Early diagnosis of prostate cancer is crucial for efficient treatment. Multi-parametric Magnetic Resonance Images (mp-MRI) are widely used for lesion detection. The Prostate Imagin…
Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies
Emma Sarfati, Alexandre Bone, Marc-Michel Rohe +2
Identifying cirrhosis is key to correctly assess the health of the liver. However, the gold standard diagnosis of the cirrhosis needs a medical intervention to obtain the histologi…
Is the U-Net Directional-Relationship Aware?
Mateus Riva, Pietro Gori, Florian Yger +1
CNNs are often assumed to be capable of using contextual information about distinct objects (such as their directional relations) inside their receptive field. However, the nature…