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

TL;DR

This paper introduces a synergic deep learning model utilizing dual convolutional neural networks with a feedback system to improve classification accuracy of diverse medical images and illustrations, outperforming previous methods.

Contribution

The paper presents a novel end-to-end synergic deep learning framework that enhances medical image classification by mutually learning representations and verifying category consistency.

Findings

Achieved state-of-the-art performance on ImageCLEF2016 dataset

Outperformed the top solution in the challenge leaderboard

Demonstrated effective handling of intra-class variation and inter-class similarity

Abstract

The Classification of medical images and illustrations in the literature aims to label a medical image according to the modality it was produced or label an illustration according to its production attributes. It is an essential and challenging research hotspot in the area of automated literature review, retrieval and mining. The significant intra-class variation and inter-class similarity caused by the diverse imaging modalities and various illustration types brings a great deal of difficulties to the problem. In this paper, we propose a synergic deep learning (SDL) model to address this issue. Specifically, a dual deep convolutional neural network with a synergic signal system is designed to mutually learn image representation. The synergic signal is used to verify whether the input image pair belongs to the same category and to give the corrective feedback if a synergic error exists. Our SDL model can be trained 'end to end'. In the test phase, the class label of an input can be predicted by averaging the likelihood probabilities obtained by two convolutional neural network components. Experimental results on the ImageCLEF2016 Subfigure Classification Challenge suggest that our proposed SDL model achieves the state-of-the art performance in this medical image classification problem and its accuracy is higher than that of the first place solution on the Challenge leader board so far.