Self-Supervised Learning Featuring Small-Scale Image Dataset for Treatable Retinal Diseases Classification
This addresses the challenge of scarce and imbalanced medical image datasets for retinal disease classification, though it is incremental as it applies existing SSL methods to a specific domain.
The study tackled the problem of automated medical diagnosis for treatable retinal diseases using small-scale OCT images, achieving a state-of-the-art accuracy of 98.84% with only 4,000 training images through a self-supervised learning model.
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.