Structured literature image finder: Extracting information from text and images in biomedical literature

  • Coelho L
  • Ahmed A
  • Arnold A
 et al. 
  • 7


    Mendeley users who have this article in their library.
  • 14


    Citations of this article.


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 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.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Luís Pedro Coelho

  • Amr Ahmed

  • Andrew Arnold

  • Joshua Kangas

  • Abdul Saboor Sheikh

  • Eric P. Xing

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free