67 citations · 74 across the 5 of their papers we have counts for
9 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…