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

TL;DR

This paper introduces a novel pretraining approach combining weakly-supervised and self-supervised learning to improve cirrhosis diagnosis from radiological and histological data, outperforming baseline methods.

Contribution

It proposes a combined loss function for pretraining that leverages large radiological datasets to enhance histological score prediction.

Findings

Achieved an AUC of 0.84 in cirrhosis prediction

Improved balanced accuracy to 0.75

Outperformed baseline classifier in key metrics

Abstract

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.