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

Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models