  Deep learning techniques are recently being used in fundus image analysis and
diabetic retinopathy detection. Microaneurysms are an important indicator of
diabetic retinopathy progression. We introduce a two-stage deep learning
approach for microaneurysms segmentation using multiple scales of the input
with selective sampling and embedding triplet loss. The model first segments on
two scales and then the segmentations are refined with a classification model.
To enhance the discriminative power of the classification model, we incorporate
triplet embedding loss with a selective sampling routine. The model is
evaluated quantitatively to assess the segmentation performance and
qualitatively to analyze the model predictions. This approach introduces a
30.29% relative improvement over the fully convolutional neural network.
