Fundamentals of digital image processing
Fundamentals of digital image processing
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
NLP found helpful (at least for one text categorization task)
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Exploring the efficacy of caption search for bioscience journal search interfaces
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Figure classification in biomedical literature to elucidate disease mechanisms, based on pathways
Artificial Intelligence in Medicine
Invited paper: Structured literature image finder: Parsing text and figures in biomedical literature
Web Semantics: Science, Services and Agents on the World Wide Web
Detecting hedge cues and their scope in biomedical text with conditional random fields
Journal of Biomedical Informatics
ISMB/ECCB'09 Proceedings of the 2009 workshop of the BioLink Special Interest Group, international conference on Linking Literature, Information, and Knowledge for Biology
Automatic figure classification in bioscience literature
Journal of Biomedical Informatics
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Bag---of---Colors for biomedical document image classification
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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A picture is worth a thousand words. Biomedical researchers tend to incorporate a significant number of images (i.e., figures or tables) in their publications to report experimental results, to present research models, and to display examples of biomedical objects. Unfortunately, this wealth of information remains virtually inaccessible without automatic systems to organize these images. We explored supervised machine-learning systems using Support Vector Machines to automatically classify images into six representative categories based on text, image, and the fusion of both. Our experiments show a significant improvement in the average F-score of the fusion classifier (73.66%) as compared with classifiers just based on image (50.74%) or text features (68.54%).