Mining Faces from Biomedical Literature using Deep Learning
Pages 562 - 567
Abstract
Gaining access to large, labelled sets of relevant images is crucial for the development and testing of biomedical imaging algorithms. Using images found in biomedical research articles would contribute some way towards a solution to this problem. However, this approach critically depends on being able to identify the most relevant images from very large sets of potentially useful figures. In this paper a deep convolutional neural network (CNN) classifier is trained using only synthetic data, to rapidly and accurately label raw images taken from biomedical articles. We apply this method in the context of detecting faces in biomedical images; and show that the classifier is able to retrieve figures containing faces with an average precision of 94.8%, from a dataset of over 31,000 images taken from articles held in the PubMed database. The utility of the classifier is then demonstrated through a case study, by aiding the mining of photographs of patients with rare genetic disorders from targeted articles. This approach is readily adaptable to facilitate the retrieval of other categories of biomedical images.
References
[1]
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman 2014. Return of the Devil in the Details: Delving Deep into Convolutional Nets Proc. BMVC.
[2]
C. Clark and S. Divvala 2016. PDFFigures 2.0: Mining figures from research papers JCDL. 143--152.
[3]
D. Demner-Fushman, S. Antani, M. Simpson, and G. Thoma. 2012. Design and development of a multimodal biomedical information retrieval system. JCSE, Vol. 6, 2 (2012), 168--177.
[4]
Q. Ferry, J. Steinberg, C. Webber, D. R. FitzPatrick, C. P. Ponting, A. Zisserman, and C. Nellraker 2014. Diagnostically relevant facial gestalt information from ordinary photos. eLIFE (Jun 2014).
[5]
W. Fleuren and W. Alkema 2015. Application of text mining in the biomedical domain. Methods Vol. 74 (2015), 97--106.
[6]
G. Gonzalez, T. Tahsin, B. Goodale, A. Greene, and C. Greene 2016. Recent advances and emerging applications in text and data mining for biomedical discovery. Brief. Bioinform., Vol. 17, 1 (2016), 33--42.
[7]
A. Gupta, A. Vedaldi, and A. Zisserman 2016. Synthetic Data for Text Localisation in Natural Images Proc. CVPR.
[8]
M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman 2014. Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition Workshop on Deep Learning, NIPS.
[9]
A. Koike and T. Takagi 2009. Classifying biomedical figures using combination of bag of keypoints and bag of words CISIS. 848--853.
[10]
A. Krizhevsky, I. Sutskever, and G. E. Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks NIPS. 1106--1114.
[11]
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel 1989. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation, Vol. 1, 4 (1989), 541--551.
[12]
T. Lehmann, M. Güld, T. Deselaers, D. Keysers, H. Schubert, K. Spitzer, H. Ney, and B. Wein. 2005. Automatic categorization of medical images for content-based retrieval and data mining. Comput Med Imaging Graph Vol. 29, 2 (2005), 143--155.
[13]
A. Rozantsev, V. Lepetit, and P. Fua 2015. On rendering synthetic images for training an object detector. Comput Vis Image Und Vol. 137 (2015), 24--37.
[14]
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, S. Huang, A. Karpathy, A. Khosla, M. Bernstein, A.C. Berg, and F.F. Li 2015. ImageNet Large Scale Visual Recognition Challenge. IJCV (2015).
[15]
A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson 2014. CNN features off-the-shelf: an astounding baseline for recognition CVPRW. 806--813.
[16]
A. Vedaldi and K. Lenc 2015. MatConvNet: Convolutional neural networks for MATLAB Proc. ACMM.
[17]
S. Xu, J. McCusker, and M. Krauthammer 2008. Yale Image Finder (YIF): a new search engine for retrieving biomedical images. Bioinformatics, Vol. 24, 17 (2008), 1968--1970.
Index Terms
- Mining Faces from Biomedical Literature using Deep Learning
Recommendations
Biomedical Mention Disambiguation using a Deep Learning Approach
BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health InformaticsAutomatically locating named entities in natural language text - named entity recognition - is an important task in the biomedical domain. Many named entity mentions are ambiguous between several bioconcept types, however, causing text spans to be ...
Deep CNN for Classification of Image Contents
IPMV '21: Proceedings of the 2021 3rd International Conference on Image Processing and Machine VisionIn recent years the classification of images has made great progress and has been used in many fields. However, it may not be possible to classify images perfectly through the CNN because of overfitting and gradient vanishing. Most existing CNNs have ...
Deep learning on small datasets using online image search
SCCG '16: Proceedings of the 32nd Spring Conference on Computer GraphicsThis paper tackles the unsolved important problem of training deep models with small amounts of annotated data. We propose a semi-supervised self-training bootstrap to deep learning on small datasets by retrieving and utilizing additional images from ...
Comments
Information & Contributors
Information
Published In
August 2017
800 pages
ISBN:9781450347228
DOI:10.1145/3107411
- General Chairs:
- Nurit Haspel,
- Lenore J. Cowen,
- Program Chairs:
- Amarda Shehu,
- Tamer Kahveci,
- Giuseppe Pozzi
Copyright © 2017 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 August 2017
Check for updates
Author Tags
Qualifiers
- Short-paper
Funding Sources
Conference
BCB '17
Sponsor:
BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
August 20 - 23, 2017
Massachusetts, Boston, USA
Acceptance Rates
ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;
Overall Acceptance Rate 254 of 885 submissions, 29%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 185Total Downloads
- Downloads (Last 12 months)3
- Downloads (Last 6 weeks)0
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in