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.

翻译：识别肝硬化是准确评估肝脏健康的关键。然而，肝硬化的金标准诊断需要医疗干预以获取组织学确认（例如METAVIR评分），因为放射学表现可能模棱两可。本研究提出利用放射科医生标注的大规模数据集（视为弱标注）进行迁移学习，以预测小规模附数据集上的组织学评分。为此，我们比较了不同预训练方法（即弱监督与自监督方法）对肝硬化预测的改进效果。最终，我们提出一种结合监督与自监督框架的预训练损失函数。该方法在METAVIR评分基线分类任务中表现更优，AUC达到0.84，平衡准确率达到0.75，而基线分类器分别为0.77和0.72。