16 citations · 41 across the 23 of their papers we have counts for
5 papers · 1 filter
Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer
Rebeca Vétil, Clément Abi-Nader, Alexandre Bône +4
We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR). To do so, we extract DLR features using a VAE while enfor…
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…
SepVAE: a contrastive VAE to separate pathological patterns from healthy ones
Robin Louiset, Edouard Duchesnay, Antoine Grigis +2
Contrastive Analysis VAE (CA-VAEs) is a family of Variational auto-encoders (VAEs) that aims at separating the common factors of variation between a background dataset (BG) (i.e.,…
Weakly-supervised positional contrastive learning: application to cirrhosis classification
Emma Sarfati, Alexandre Bône, Marc-Michel Rohé +2
Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e.g., radiological scores). Access to high-confidence labels, such as histolog…
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…