17 citations · 26 across the 8 of their papers we have counts for
6 papers · 1 filter
MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network
Théo Estienne, Maria Vakalopoulou, Enzo Battistella +5
Deformable registration consists of finding the best dense correspondence between two different images. Many algorithms have been published, but the clinical application was made d…
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…
Exploring Deep Registration Latent Spaces
Théo Estienne, Maria Vakalopoulou, Stergios Christodoulidis +8
Explainability of deep neural networks is one of the most challenging and interesting problems in the field. In this study, we investigate the topic focusing on the interpretabilit…
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…
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…