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Identifying cirrhosis is key to correctly assess the health of the liver.However, the gold standard diagnosis of the cirrhosis needs a medicalintervention to obtain the histological confirmation, e.g. the METAVIR score,as the radiological presentation can be equivocal. In this work, we propose toleverage transfer learning from large datasets annotated by radiologists, whichwe consider as a weak annotation, to predict the histological score availableon a small annex dataset. To this end, we propose to compare differentpretraining methods, namely weakly-supervised and self-supervised ones, toimprove the prediction of the cirrhosis. Finally, we introduce a loss functioncombining both supervised and self-supervised frameworks for pretraining. Thismethod outperforms the baseline classification of the METAVIR score, reachingan AUC of 0.84 and a balanced accuracy of 0.75, compared to 0.77 and 0.72 for abaseline classifier.

Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies