Self-Supervised Learning Featuring Small-Scale Image Dataset for Treatable Retinal Diseases Classification

arXiv:2404.10166v1 Announce Type: cross
Abstract: 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 …

abstract annotation arxiv automated classification costs cs.cv cs.lg dataset diagnosis diseases good image images labor medical networks neural networks scale self-supervised learning small ssl supervised learning through transfer transfer learning type