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Diabetic Retinopathy (DR) is one of the leading causes of preventable blindness in the developed world. With the increasing number of diabetic patients there is a growing need of an automated system for DR detection. We propose EyeWeS, a general methodology that enables the conversion of any pre-trained convolutional neural network into a weakly-supervised model while at the same time achieving an increased performance and efficiency. Via EyeWeS, we are able to design a new family of methodsdoi:10.23919/mva.2019.8757991 dblp:conf/mva/0005AAGMSC19 fatcat:cx5aoahzurbpfdk2y4hwviog3e