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Information 2017, 8(2), 48; doi:10.3390/info8020048

Assembling Deep Neural Networks for Medical Compound Figure Detection

1
School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
2
School of Computer Science & Engineering, Dalian Minzu University, Dalian 116600, China
3
School of Software Engineering, Dalian University of Foreign Language, Dalian 116044, China
*
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Received: 17 March 2017 / Revised: 18 April 2017 / Accepted: 19 April 2017 / Published: 21 April 2017
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Abstract

Compound figure detection on figures and associated captions is the first step to making medical figures from biomedical literature available for further analysis. The performance of traditional methods is limited to the choice of hand-engineering features and prior domain knowledge. We train multiple convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent unit (GRU) networks on top of pre-trained word vectors to learn textual features from captions and employ deep CNNs to learn visual features from figures. We then identify compound figures by combining textual and visual prediction. Our proposed architecture obtains remarkable performance in three run types—textual, visual and mixed—and achieves better performance in ImageCLEF2015 and ImageCLEF2016. View Full-Text
Keywords: compound figure detection; convolutional neural network; recurrent neural network; word vectors compound figure detection; convolutional neural network; recurrent neural network; word vectors
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yu, Y.; Lin, H.; Meng, J.; Wei, X.; Zhao, Z. Assembling Deep Neural Networks for Medical Compound Figure Detection. Information 2017, 8, 48.

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