67 citations · 85 across the 8 of their papers we have counts for
10 papers
Giga-SSL: Self-Supervised Learning for Gigapixel Images
Tristan Lazard, Marvin Lerousseau, Etienne Decencière +1
Whole slide images (WSI) are microscopy images of stained tissue slides routinely prepared for diagnosis and treatment selection in medical practice. WSI are very large (gigapixel…
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…
SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classification
Marvin Lerousseau, Maria Vakalopoulou, Eric Deutsch +1
Multiple instance learning (MIL) is the preferred approach for whole slide image classification. However, most MIL approaches do not exploit the interdependencies of tiles extracte…
Design and implementation of an environment for Learning to Run a Power Network (L2RPN)
Marvin Lerousseau
This report summarizes work performed as part of an internship at INRIA, in partial requirement for the completion of a master degree in math and informatics. The goal of the inter…
Cancer Gene Profiling through Unsupervised Discovery
Enzo Battistella, Maria Vakalopoulou, Roger Sun +9
Precision medicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of genes as well as the…
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…
Deep learning based registration using spatial gradients and noisy segmentation labels
Théo Estienne, Maria Vakalopoulou, Enzo Battistella +6
Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based approaches became quite popular, providing fast and p…
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…
Learning to run a power network challenge for training topology controllers
Antoine Marot, Benjamin Donnot, Camilo Romero +4
For power grid operations, a large body of research focuses on using generation redispatching, load shedding or demand side management flexibilities. However, a less costly and pot…