10 citations · 16 across the 4 of their papers we have counts for
5 papers
Controllable Latent Space Augmentation for Digital Pathology
Sofiène Boutaj, Marin Scalbert, Pierre Marza +3
Whole slide image (WSI) analysis in digital pathology presents unique challenges due to the gigapixel resolution of WSIs and the scarcity of dense supervision signals. While Multip…
Distilling foundation models for robust and efficient models in digital pathology
Alexandre Filiot, Nicolas Dop, Oussama Tchita +8
In recent years, the advent of foundation models (FM) for digital pathology has relied heavily on scaling the pre-training datasets and the model size, yielding large and powerful…
Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization
Marin Scalbert, Maria Vakalopoulou, Florent Couzinié-Devy
In Self-Supervised Learning (SSL), models are typically pretrained, fine-tuned, and evaluated on the same domains. However, they tend to perform poorly when evaluated on unseen dom…
Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology
Marin Scalbert, Maria Vakalopoulou, Florent Couzinié-Devy
Histopathology whole slide images (WSIs) can reveal significant inter-hospital variability such as illumination, color or optical artifacts. These variations, caused by the use of…
Multi-Source domain adaptation via supervised contrastive learning and confident consistency regularization
Marin Scalbert, Maria Vakalopoulou, Florent Couzinié-Devy
Multi-Source Unsupervised Domain Adaptation (multi-source UDA) aims to learn a model from several labeled source domains while performing well on a different target domain where on…