Publications by authors named "Paul Tourniaire"
Purpose: Immune checkpoint inhibitors (ICIs) are now one of the standards of care for patients with lung cancer and have greatly improved both progression-free and overall survival, although of the patients respond to the treatment, and some face acute adverse events. Although a few predictive biomarkers have integrated the clinical workflow, they require additional modalities on top of whole-slide images and lack efficiency or robustness. In this work, we propose a biomarker of immunotherapy outcome derived solely from the analysis of histology slides.
View Article and Find Full Text PDFGiven the size of digitized Whole Slide Images (WSIs), it is generally laborious and time-consuming for pathologists to exhaustively delineate objects within them, especially with datasets containing hundreds of slides to annotate. Most of the time, only slide-level labels are available, giving rise to the development of weakly-supervised models. However, it is often difficult to obtain from such models accurate object localization, e.
View Article and Find Full Text PDFArticle Synopsis
- * Researchers implemented a deep learning classifier using a convolutional neural network (CNN) to assist pathologists in diagnosing these cancers, training it on a dataset of 18,752 image tiles from 60 slides, which were previously diagnosed by expert pathologists.
- * The CNN model achieved an impressive mean F1-score of 0.99 during testing, indicating its potential to effectively support pathologists in accurately differentiating between these challenging cancer types,