3 citations · 5 across the 5 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…
Evaluation of the mitotic score of invasive breast carcinomas on digital slide: development and contribution of a mitosis detection algorithm
Loris Guichard, Clara Simmat, Margot Dupeux +6
Introduction: Nottingham grading system is a major prognostic factor for invasive breast carcinoma (IBC). Its determination requires the evaluation of the mitotic score (MS) which…
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…
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…
Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology
Nicolas Nerrienet, Rémy Peyret, Marie Sockeel +1
Generalization is one of the main challenges of computational pathology. Slide preparation heterogeneity and the diversity of scanners lead to poor model performance when used on d…