68 citations · 126 across the 6 of their papers we have counts for
6 papers
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…
Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers
Charlie Saillard, Olivier Dehaene, Tanguy Marchand +4
Microsatellite instability (MSI) is a tumor phenotype whose diagnosis largely impacts patient care in colorectal cancers (CRC), and is associated with response to immunotherapy in…
Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Images
Kathryn Schutte, Olivier Moindrot, Paul Hérent +2
As AI-based medical devices are becoming more common in imaging fields like radiology and histology, interpretability of the underlying predictive models is crucial to expand their…
ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors
Mikhail Zaslavskiy, Simon Jégou, Eric W. Tramel +1
Timely assessment of compound toxicity is one of the biggest challenges facing the pharmaceutical industry today. A significant proportion of compounds identified as potential lead…
Image Segmentation by Iterative Inference from Conditional Score Estimation
Adriana Romero, Michal Drozdzal, Akram Erraqabi +2
Inspired by the combination of feedforward and iterative computations in the virtual cortex, and taking advantage of the ability of denoising autoencoders to estimate the score of…
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
Simon Jégou, Michal Drozdzal, David Vazquez +2
State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampli…