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Assembling Deep Neural Networks for Medical Compound Figure Detection
- Source :
- Information; Volume 8; Issue 2; Pages: 48
- Publication Year :
- 2017
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2017.
-
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.
- Subjects :
- Computer science
business.industry
Speech recognition
02 engineering and technology
compound figure detection
convolutional neural network
recurrent neural network
word vectors
Convolutional neural network
03 medical and health sciences
0302 clinical medicine
Recurrent neural network
030228 respiratory system
0202 electrical engineering, electronic engineering, information engineering
Domain knowledge
Deep neural networks
020201 artificial intelligence & image processing
Artificial intelligence
business
Word (computer architecture)
Information Systems
Subjects
Details
- Language :
- English
- ISSN :
- 20782489
- Database :
- OpenAIRE
- Journal :
- Information; Volume 8; Issue 2; Pages: 48
- Accession number :
- edsair.doi.dedup.....84ecd35ae1e4a769faca9012406f28c5
- Full Text :
- https://doi.org/10.3390/info8020048