Výsledky vyhledávání - "Peyret, Remy"

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Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image Classification

Autor: Dedieu, Lucas, Nerrienet, Nicolas, Nivaggioli, Adrien, Simmat, Clara, Clavel, Marceau, Gauthier, Arnaud, Sockeel, Stéphane, Peyret, Rémy

Recent advancements in deep learning have proven highly effective in medical image classification, notably within histopathology. However, noisy labels represent a critical challenge in histopathology image classification, where accurate annotations

Externí odkaz: http://arxiv.org/abs/2404.07605

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Classification in Histopathology: A unique deep embeddings extractor for multiple classification tasks

Autor: Nivaggioli, Adrien, Pozin, Nicolas, Peyret, Rémy, Sockeel, Stéphane, Sockeel, Marie, Nerrienet, Nicolas, Clavel, Marceau, Simmat, Clara, Miquel, Catherine

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 certain biomarkers or classify lesions. However, such techniques require lar

Externí odkaz: http://arxiv.org/abs/2303.05180

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Convolutional Neural Network-Based Automatic Classification of Colorectal and Prostate Tumor Biopsies Using Multispectral Imagery: System Development Study

Autor: Peyret, Remy, alSaeed, Duaa, Khelifi, Fouad, Al-Ghreimil, Nadia, Al-Baity, Heyam, Bouridane, Ahmed

Publikováno v: JMIR Bioinform Biotech 2022

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 needle biopsy samples. This manual process is time-consuming and error

Externí odkaz: http://arxiv.org/abs/2301.13151

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Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology

Autor: Nerrienet, Nicolas, Peyret, Rémy, Sockeel, Marie, Sockeel, Stéphane

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 data from medical centers not seen during training. In order to achieve

Externí odkaz: http://arxiv.org/abs/2301.13128

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Multicenter automatic detection of invasive carcinoma on breast whole slide images

Autor: Peyret, Rémy, Pozin, Nicolas, Sockeel, Stéphane, Kammerer-Jacquet, Solène-Florence, Adam, Julien, Bocciarelli, Claire, Ditchi, Yoan, Bontoux, Christophe, Depoilly, Thomas, Guichard, Loris, Lanteri, Elisabeth, Sockeel, Marie, Prévot, Sophie

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 to manage the increasing workload. In this context, artificial intellig

Externí odkaz: http://arxiv.org/abs/2301.06789

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Dissertation/ Thesis

Automated classification of cancer tissues using multispectral imagery

Autor: Peyret, Remy

Automated classification of medical images for colorectal and prostate cancer diagnosis is a crucial tool for improving routine diagnosis decisions. Therefore, in the last few decades, there has been an increasing interest in refining and adapting ma

Externí odkaz: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757270

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