Multi-expert deep networks for multi-disease detection in retinal fundus images.

Automatic diagnosis of eye diseases from retinal fundus images is quite challenging. Common public datasets include images of subjects with multiple diseases with uneven distribution of labels. Rare diseases are especially challenging due to their under-representation in such datasets. In this paper, we propose a training pipeline for the multi-labeled classification with uneven distribution of the sample size and sample difficulty. First, we guide the training of the initial model by weighing the training loss using an inverse-frequency for each class. This will balance the training on over-represented and under-represented samples. We then adjust the class weights using the aggregated loss for each class, and train for more iterations. In this way, the model at each iteration will focus more on difficult samples and cover the shortcomings of the previous model. Finally, we ensemble together all the models using out proposed Heuristic Stacking algorithm for improving multi-label predictions beyond simple averaging. Our experimental results on the Retinal Image Analysis for Multi-Disease Detection(RIADD)-2021 challenge dataset show that the proposed approach achieves a 88.24 % accuracy score, which is competitive with the top three ranked methods of the competition. Furthermore, we perform ablation study to stress-test our Heuristic Stacking ensemble methods versus classical methods such as bagging n multi-label classification problems.