  Age-related Macular Degeneration (AMD) is a leading cause of blindness.
Although the Age-Related Eye Disease Study group previously developed a 9-step
AMD severity scale for manual classification of AMD severity from color fundus
images, manual grading of images is time-consuming and expensive. Built on our
previous work DeepSeeNet, we developed a novel deep learning model for
automated classification of images into the 9-step scale. Instead of predicting
the 9-step score directly, our approach simulates the reading center grading
process. It first detects four AMD characteristics (drusen area, geographic
atrophy, increased pigment, and depigmentation), then combines these to derive
the overall 9-step score. Importantly, we applied multi-task learning
techniques, which allowed us to train classification of the four
characteristics in parallel, share representation, and prevent overfitting.
Evaluation on two image datasets showed that the accuracy of the model exceeded
the current state-of-the-art model by > 10%.
