Paper Info
Title
Modality Classification for Searching Figures in Biomedical Literature
Abstract
Image modality classification categorizes images according to their type. It is an important module in the Open-iSM multimodal (text+image) search engine that retrieves figures from biomedical articles. It is a hierarchical classification where on the top level the input figures are classified into two general categories: regular images (X-ray, CT, MRI, photographs, etc.) vs. illustration images (cartoon sketch, charts, graphs, etc.). This binary classification task is challenged by the vast diversity of visual material (image type), and the way it is organized (simple or compound figures). We present two methods for this binary classification: (i) Support Vector Machines (SVM) with manually-selected features, including a feature based on semantic concepts, and, (ii) Deep Learning method which avoids the process of feature handcrafting. Both methods were tested and compared on a dataset of 16400 figures. Both methods achieved good performance (above 95% accuracy). The slightly better performance of the feature-based method demonstrates the effectiveness of the features we chose.
Year
DOI
Venue
2016
10.1109/CBMS.2016.29
2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)
Keywords
Field
DocType
Modality classification,figure searching,concept feature,support vector machine,deep learning,convolutional neural networks
Data mining,Binary classification,Convolutional neural network,Computer science,Artificial intelligence,Deep learning,Artificial neural network,Sketch,Computer vision,Pattern recognition,Visualization,Support vector machine,Semantics
Conference
ISSN
ISBN
Citations 
2372-9198
978-1-4673-9037-8
0
PageRank 
References 
Authors
0.34
13
6
Name
Order
Citations
PageRank
Zhiyun Xue124522.97
Md. Mahmudur Rahman265250.91
Sameer Antani31402134.03
L. Rodney Long453456.98
Dina Demner Fushman51717147.70
George R. Thoma61207132.81