Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies - Laboratoire Traitement et Communication de l'Information Access content directly
Conference Papers Year : 2023

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

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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Dates and versions

hal-03993204 , version 1 (16-02-2023)

Identifiers

  • HAL Id : hal-03993204 , version 1

Cite

Emma Sarfati, Alexandre Bone, Marc-Michel Rohe, Pietro Gori, Isabelle Bloch. Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies. IEEE ISBI, Apr 2023, Cartagena de Indias, Colombia. ⟨hal-03993204⟩
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