Article

Self-supervised retinal thickness prediction enables deep learning from unlabeled data to boost classification of diabetic retinopathy

2019-12-02

Abstract excerpt

Access to large, annotated samples represents a considerable challenge for training accurate deep-learning models in medical imaging. While current leading-edge transfer learning from pre-trained models can help with cases lacking data, it limits design choices, and generally results in the use of unnecessarily large models. We propose a novel, self-supervised training scheme for obtaining high-quality, pre-traine...

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Literature Corpus work: 515fdece-213b-5c55-857e-c7a60c66d794
DOI: 10.1101/861757

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