bibtext: @InProceedings{ Mor_MultiVarNet_MICCAI2024,
                   author = { Morel, Louis-Oscar and Muzammel, Muhammad and Vinçon, Nathan and Derangère, Valentin and Ladoire, Sylvain and Rittscher, Jens },
                   title = { { MultiVarNet - Predicting Tumour Mutational status at the Protein Level } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15003 },
                   month = {October},
                   pages = { pending },
                }
abstract: Deep learning research in medical image analysis demonstrated the capability of predicting molecular information, including tumour mutational status, from cell and tissue morphology extracted from standard histology images. While this capability holds the promise of revolutionising pathology, it is of critical importance to go beyond gene-level mutations and develop methodologies capable of predicting precise variant mutations. Only then will it be possible to support important clinical applications, including specific targeted therapies.
title: MultiVarNet - Predicting Tumour Mutational status at the Protein Level
authors: Morel, Louis-Oscar Muzammel, Muhammad Vinçon, Nathan Derangère, Valentin Ladoire, Sylvain Rittscher, Jens