Predicting survival after hepatocellular carcinoma resection using deep-learning on histological slides
Charlie Saillard(Laboratoire Procédés et Ingénierie en Mécanique et Matériaux), Julien Caldéraro(Inserm), Alain Luciani(Université Paris-Est Créteil), Marianne Ziol(Assistance Publique – Hôpitaux de Paris), Sebastien Mulé(Assistance Publique – Hôpitaux de Paris), Hélène Regnault(Assistance Publique – Hôpitaux de Paris), Pierre Courtiol, Elodie Pronier, Mikhail Zaslavskiy, Sylvain Toldo, Matahi Moarii, Jean–Michel Pawlotsky(Inserm), Thomas Clozel(NewYork–Presbyterian Hospital), Gilles Wainrib(Larkin University), Giuliana Amaddeo(Assistance Publique – Hôpitaux de Paris), Alexis Laurent(Hôpitaux Universitaires Henri-Mondor), Oumeima Laifa, Danièle Sommacale(Université Paris-Est Créteil), Benoît Schmauch(Owl Research Institute)
Cited by 25
Related Papers
Potential of fecal microbiota for early‐stage detection of colorectal cancer
|Molecular Systems Biology|2014|1.4k
Personalised versus standard dosimetry approach of selective internal radiation therapy in patients with locally advanced hepatocellular carcinoma (DOSISPHERE-01): a randomised, multicentre, open-label phase 2 trial
|The Lancet. Gastroenterology & hepatology|2020|602
A deep learning model to predict RNA-Seq expression of tumours from whole slide images
|Nature Communications|2020|524
Predicting Survival After Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides
|Hepatology|2020|309
Impact of COVID-19 on global HCV elimination efforts
|Journal of Hepatology|2020|279