Self-Supervised Learning Featuring Small-Scale Image Dataset for
  Treatable Retinal Diseases Classification
Luffina C. Huang Darren J. Chiu Manish Mehta
Automated medical diagnosis through image-based neural networks has increased
in popularity and matured over years. Nevertheless, it is confined by the
scarcity of medical images and the expensive labor annotation costs.
Self-Supervised Learning (SSL) is an good alternative to Transfer Learning (TL)
and is suitable for imbalanced image datasets. In this study, we assess four
pretrained SSL models and two TL models in treatable retinal diseases
classification using small-scale Optical Coherence Tomography (OCT) images
ranging from 125 to 4000 with balanced or imbalanced distribution for training.
The proposed SSL model achieves the state-of-art accuracy of 98.84% using only
4,000 training images. Our results suggest the SSL models provide superior
performance under both the balanced and imbalanced training scenarios. The SSL
model with MoCo-v2 scheme has consistent good performance under the imbalanced
scenario and, especially, surpasses the other models when the training set is
less than 500 images.