  In this paper, we propose a framework that incorporates experts diagnostics
and insights into the analysis of Optical Coherence Tomography (OCT) using
multi-modal learning. To demonstrate the effectiveness of this approach, we
create a medical diagnostic attribute dataset to improve disease classification
using OCT. Although there have been successful attempts to deploy machine
learning for disease classification in OCT, such methodologies lack the experts
insights. We argue that injecting ophthalmological assessments as another
supervision in a learning framework is of great importance for the machine
learning process to perform accurate and interpretable classification. We
demonstrate the proposed framework through comprehensive experiments that
compare the effectiveness of combining diagnostic attribute features with
latent visual representations and show that they surpass the state-of-the-art
approach. Finally, we analyze the proposed dual-stream architecture and provide
an insight that determine the components that contribute most to classification
performance.
