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Deep convolutional networks have recently shown very interesting performance in a variety of computer vision tasks. Besides network architecture optimization, a key contribution to their success is the availability of training data. Network training is usually done with manually validated data but this approach has a significant cost and poses a scalability problem. Here we introduce an innovative pipeline that combines weakly-supervised image reranking methods and network finetuning todoi:10.1109/cbmi.2015.7153607 dblp:conf/cbmi/VoGBP15 fatcat:sejc5m4wabczdeoas7wquivkdu