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

被引：0

作者：

Soroosh Tayebi Arasteh

Leo Misera

Jakob Nikolas Kather

Daniel Truhn

Sven Nebelung

机构：

[1] University Hospital RWTH Aachen,Department of Diagnostic and Interventional Radiology

[2] Faculty of Medicine and University Hospital Carl Gustav Carus Dresden,Institute and Polyclinic for Diagnostic and Interventional Radiology

[3] Technische Universität Dresden,Else Kröner Fresenius Center for Digital Health

[4] Technische Universität Dresden,Department of Medicine III

[5] University Hospital RWTH Aachen,Medical Oncology, National Center for Tumor Diseases (NCT)

[6] University Hospital Heidelberg,undefined

来源：

European Radiology Experimental | / 8卷

关键词：

Artificial intelligence; Deep learning; Medical image processing; Radiography (thoracic); Unsupervised machine learning;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

• Validated on over 800,000 chest radiographs from 6 datasets and 20 imaging findings, a self-supervised pretraining on non-medical images outperformed ImageNet-based supervised pretraining.

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