Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histology

no code implementations • 7 Dec 2020 • Olivier Dehaene, Axel Camara, Olivier Moindrot, Axel de Lavergne, Pierre Courtiol

We identify this as a key weakness and propose to train an in-domain feature extractor on histology images using MoCo v2, a recent self-supervised learning algorithm.

BIG-bench Machine Learning Self-Supervised Learning +1

Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach

no code implementations • ICLR 2018 • Pierre Courtiol, Eric W. Tramel, Marc Sanselme, Gilles Wainrib

Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard.

General Classification Multiple Instance Learning +5

Robust Detection of Covariate-Treatment Interactions in Clinical Trials

no code implementations • 21 Dec 2017 • Baptiste Goujaud, Eric W. Tramel, Pierre Courtiol, Mikhail Zaslavskiy, Gilles Wainrib

Detection of interactions between treatment effects and patient descriptors in clinical trials is critical for optimizing the drug development process.