  In this work, we propose an advanced AI based grading system for OCT images.
The proposed system is a very deep fully convolutional attentive classification
network trained with end to end advanced transfer learning with online random
augmentation. It uses quasi random augmentation that outputs confidence values
for diseases prevalence during inference. Its a fully automated retinal OCT
analysis AI system capable of pathological lesions understanding without any
offline preprocessing/postprocessing step or manual feature extraction. We
present a state of the art performance on the publicly available Mendeley OCT
dataset.
