10 citations · 14 across the 4 of their papers we have counts for
4 papers · 1 filter
Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image Classification
Lucas Dedieu, Nicolas Nerrienet, Adrien Nivaggioli +5
Recent advancements in deep learning have proven highly effective in medical image classification, notably within histopathology. However, noisy labels represent a critical challen…
Classification in Histopathology: A unique deep embeddings extractor for multiple classification tasks
Adrien Nivaggioli, Nicolas Pozin, Rémy Peyret +6
In biomedical imaging, deep learning-based methods are state-of-the-art for every modality (virtual slides, MRI, etc.) In histopathology, these methods can be used to detect certai…
Weakly Supervised Semantic Segmentation of Satellite Images
Adrien Nivaggioli, Hicham Randrianarivo
When one wants to train a neural network to perform semantic segmentation, creating pixel-level annotations for each of the images in the database is a tedious task. If he works wi…
Image search using multilingual texts: a cross-modal learning approach between image and text
Maxime Portaz, Hicham Randrianarivo, Adrien Nivaggioli +3
Multilingual (or cross-lingual) embeddings represent several languages in a unique vector space. Using a common embedding space enables for a shared semantic between words from dif…