188 citations · 400 across the 22 of their papers we have counts for
9 papers · 1 filter
Certification of Deep Learning Models for Medical Image Segmentation
Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon +3
In medical imaging, segmentation models have known a significant improvement in the past decade and are now used daily in clinical practice. However, similar to classification mode…
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle +35
Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Publ…
Region-guided CycleGANs for Stain Transfer in Whole Slide Images
Joseph Boyd, Irène Villa, Marie-Christine Mathieu +4
In whole slide imaging, commonly used staining techniques based on hematoxylin and eosin (H&E) and immunohistochemistry (IHC) stains accentuate different aspects of the tissue land…
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology
Joseph Boyd, Mykola Liashuha, Eric Deutsch +3
The examination of histopathology images is considered to be the gold standard for the diagnosis and stratification of cancer patients. A key challenge in the analysis of such imag…
Weakly supervised pan-cancer segmentation tool
Marvin Lerousseau, Marion Classe, Enzo Battistella +8
The vast majority of semantic segmentation approaches rely on pixel-level annotations that are tedious and time consuming to obtain and suffer from significant inter and intra-expe…
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution
Theophraste Henry, Alexandre Carre, Marvin Lerousseau +4
Brain tumor segmentation is a critical task for patient's disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks, mainly…
Multimodal brain tumor classification
Marvin Lerousseau, Eric Deutsh, Nikos Paragios
Cancer is a complex disease that provides various types of information depending on the scale of observation. While most tumor diagnostics are performed by observing histopathologi…
Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention
Mihir Sahasrabudhe, Stergios Christodoulidis, Roberto Salgado +5
Segmentation and accurate localization of nuclei in histopathological images is a very challenging problem, with most existing approaches adopting a supervised strategy. These meth…
Weakly supervised multiple instance learning histopathological tumor segmentation
Marvin Lerousseau, Maria Vakalopoulou, Marion Classe +7
Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice. State of the art methods rely on…