Title
Structured literature image finder: extracting information from text and images in biomedical literature
Abstract
Slif uses a combination of text-mining and image processing to extract information from figures in the biomedical literature. It also uses innovative extensions to traditional latent topic modeling to provide new ways to traverse the literature. Slif provides a publicly available searchable database (http://slif.cbi.cmu.edu). Slif originally focused on fluorescence microscopy images. We have now extended it to classify panels into more image types. We also improved the classification into subcellular classes by building a more representative training set. To get the most out of the human labeling effort, we used active learning to select images to label. We developed models that take into account the structure of the document (with panels inside figures inside papers) and the multi-modality of the information (free and annotated text, images, information from external databases). This has allowed us to provide new ways to navigate a large collection of documents.
Year
DOI
Venue
2008
10.1007/978-3-642-13131-8_4
BioLINK@ISMB/ECCB
Keywords
Field
DocType
new way,biomedical literature,fluorescence microscopy image,image processing,image type,annotated text,available searchable database,external databases,innovative extension,large collection,structured literature image finder
Training set,Active learning,Information retrieval,Computer science,Optical character recognition,Image processing,Topic model,Bioinformatics,Stepwise discriminant analysis,Traverse
Conference
Volume
ISSN
ISBN
6004
0302-9743
3-642-13130-1
Citations 
PageRank 
References 
9
0.71
14
Authors
8
Name
Order
Citations
PageRank
Luis Pedro Coelho1907.15
Amr Ahmed2174392.13
Andrew Arnold318710.50
Joshua Kangas4403.00
Abdul-Saboor Sheikh5376.40
Bo Xing67332471.43
William W. Cohen7101781243.74
Robert F Murphy885178.19