  Preventable or undiagnosed visual impairment and blindness affect billion of
people worldwide. Automated multi-disease detection models offer great
potential to address this problem via clinical decision support in diagnosis.
In this work, we proposed an innovative multi-disease detection pipeline for
retinal imaging which utilizes ensemble learning to combine the predictive
capabilities of several heterogeneous deep convolutional neural network models.
Our pipeline includes state-of-the-art strategies like transfer learning, class
weighting, real-time image augmentation and Focal loss utilization.
Furthermore, we integrated ensemble learning techniques like heterogeneous deep
learning models, bagging via 5-fold cross-validation and stacked logistic
regression models. Through internal and external evaluation, we were able to
validate and demonstrate high accuracy and reliability of our pipeline, as well
as the comparability with other state-of-the-art pipelines for retinal disease
prediction.
