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

• URL: http://arxiv.org/abs/2302.08427v1
• Date: Thu, 16 Feb 2023 17:06:23 GMT
• Title: Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies
• Authors: Emma Sarfati, Alexandre Bone, Marc-Michel Rohe, Pietro Gori, Isabelle Bloch
• Abstract summary: We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
• Score: 62.840338941861134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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