  Identifying cirrhosis is key to correctly assess the health of the liver.
However, the gold standard diagnosis of the cirrhosis needs a medical
intervention to obtain the histological confirmation, e.g. the METAVIR score,
as the radiological presentation can be equivocal. In this work, we propose to
leverage transfer learning from large datasets annotated by radiologists, which
we consider as a weak annotation, to predict the histological score available
on a small annex dataset. To this end, we propose to compare different
pretraining methods, namely weakly-supervised and self-supervised ones, to
improve the prediction of the cirrhosis. Finally, we introduce a loss function
combining both supervised and self-supervised frameworks for pretraining. This
method outperforms the baseline classification of the METAVIR score, reaching
an AUC of 0.84 and a balanced accuracy of 0.75, compared to 0.77 and 0.72 for a
baseline classifier.
