  Is self-supervised deep learning (DL) for medical image analysis already a
serious alternative to the de facto standard of end-to-end trained supervised
DL? We tackle this question for medical image classification, with a particular
focus on one of the currently most limiting factors of the field: the
(non-)availability of labeled data. Based on three common medical imaging
modalities (bone marrow microscopy, gastrointestinal endoscopy, dermoscopy) and
publicly available data sets, we analyze the performance of self-supervised DL
within the self-distillation with no labels (DINO) framework. After learning an
image representation without use of image labels, conventional machine learning
classifiers are applied. The classifiers are fit using a systematically varied
number of labeled data (1-1000 samples per class). Exploiting the learned image
representation, we achieve state-of-the-art classification performance for all
three imaging modalities and data sets with only a fraction of between 1% and
10% of the available labeled data and about 100 labeled samples per class.
