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.

本研究通过采用 DINO 框架，尤其是使用非监督式学习中的图像表示来分析了医学图像分类中的无监督深度学习性能，并证明这种方法可以在使用少量标记数据（大约每类 100 个标记样本）的情况下，取得超过现有技术的性能表现。