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The Classification of medical images and illustrations in the literature aimsto label a medical image according to the modality it was produced or label anillustration according to its production attributes. It is an essential andchallenging research hotspot in the area of automated literature review,retrieval and mining. The significant intra-class variation and inter-classsimilarity caused by the diverse imaging modalities and various illustrationtypes brings a great deal of difficulties to the problem. In this paper, wepropose a synergic deep learning (SDL) model to address this issue.Specifically, a dual deep convolutional neural network with a synergic signalsystem is designed to mutually learn image representation. The synergic signalis used to verify whether the input image pair belongs to the same category andto give the corrective feedback if a synergic error exists. Our SDL model canbe trained 'end to end'. In the test phase, the class label of an input can bepredicted by averaging the likelihood probabilities obtained by twoconvolutional neural network components. Experimental results on theImageCLEF2016 Subfigure Classification Challenge suggest that our proposed SDLmodel achieves the state-of-the art performance in this medical imageclassification problem and its accuracy is higher than that of the first placesolution on the Challenge leader board so far.

Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning