3 citations · 8 across the 4 of their papers we have counts for
4 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…
Convolutional Neural Network-Based Automatic Classification of Colorectal and Prostate Tumor Biopsies Using Multispectral Imagery: System Development Study
Remy Peyret, Duaa alSaeed, Fouad Khelifi +3
Colorectal and prostate cancers are the most common types of cancer in men worldwide. To diagnose colorectal and prostate cancer, a pathologist performs a histological analysis on…
Multicenter automatic detection of invasive carcinoma on breast whole slide images
Rémy Peyret, Nicolas Pozin, Stéphane Sockeel +10
Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required t…