A Transformer-Based Anomaly Detection System for OCT Image Embeddings

Retinopathy is characterized by pathological alterations in the retina that can lead to partial or complete vision loss. It can result from a variety of causes, including diabetes, hypertension, and autoimmune disorders, such as Autoimmune Retinopathy, which is particularly challenging to detect and diagnose. Due to its visually similar features, Autoimmune Retinopathy is often mistaken for other retinal diseases that require distinct treatments. The objective of this work is to develop a system for detecting clinically relevant anomalies in retinal images using a Transformer-based Deep Learning model. By leveraging Transformers, the model captures subtle variations in retinal images, enhancing diagnostic accuracy. This approach aims to assist ophthalmologists in precise disease detection, improving early diagnosis and enabling personalized treatment strategies.