10 citations · 14 across the 4 of their papers we have counts for
5 papers
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…
Animation Synthesis Triggered by Vocal Mimics
Adrien Nivaggioli, Damien Rohmer
We propose a method leveraging the naturally time-related expressivity of our voice to control an animation composed of a set of short events. The user records itself mimicking ono…
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…