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. Applying a patch-wise approach with healthy patches only sampled from healthy patient images gives the ability of learning segmentation even in cases where not all instances of a lesion are annotated in the gold standard images. This approach introduces a 30.29% improvement over the fully convolutional neural network.     «

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. Applying a patch-wise approach with healthy patches only sampled from healthy patient images gives the ability of learning segmentation even in...     »