Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images.

ABSTRACT:

Background

Pretraining labeled datasets, like ImageNet, have become a technical standard in advanced medical image analysis. However, the emergence of self-supervised learning (SSL), which leverages unlabeled data to learn robust features, presents an opportunity to bypass the intensive labeling process. In this study, we explored if SSL for pretraining on non-medical images can be applied to chest radiographs and how it compares to supervised pretraining on non-medical images and on medical images.

Methods

We utilized a vision transformer and initialized its weights based on the following: (i) SSL pretraining on non-medical images (DINOv2), (ii) supervised learning (SL) pretraining on non-medical images (ImageNet dataset), and (iii) SL pretraining on chest radiographs from

SUBMITTER: Tayebi Arasteh S 

PROVIDER: S-EPMC10850044 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature