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Utilizing image and caption information for biomedical document classification.

Pengyuan Li1, Xiangying Jiang1,2, Gongbo Zhang1,3

  • 1Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.

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Summary
This summary is machine-generated.

This study introduces a new method for classifying biomedical research papers using images and text. Combining these elements significantly improves the accuracy of identifying relevant scientific literature for databases.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Scientific Literature Analysis

Background:

  • Biomedical research findings are disseminated through publications, necessitating efficient literature curation for databases.
  • Manual curation is labor-intensive, making automated identification of relevant articles crucial for accelerating research.
  • Current methods primarily rely on textual content from titles and abstracts, often overlooking valuable image and caption information.

Purpose of the Study:

  • To develop an automated document classification scheme incorporating image and caption data alongside traditional text.
  • To introduce a novel image representation, "Figure-word", for effective utilization of visual information.
  • To enhance the accuracy and efficiency of identifying relevant biomedical publications for database curation.

Main Methods:

  • A new document classification scheme was developed, integrating information from titles-and-abstracts, captions, and images.
  • A novel image representation, "Figure-word", was introduced, derived from subfigure class labels.
  • Word embeddings were used for textual data (captions and titles-and-abstracts).
  • Two information integration methods were explored: feature vector combination and meta-classification.

Main Results:

  • The proposed "Figure-word" representation effectively captures image information for document classification.
  • Captions and titles-and-abstracts provide complementary information, enhancing classification performance.
  • Combining "Figure-words", captions, and titles-and-abstracts led to significantly improved overall document classification accuracy.
  • The study demonstrates the value of multimodal information (images, captions, text) in biomedical literature analysis.

Conclusions:

  • The integration of image and caption data, represented by "Figure-words" and word embeddings, substantially improves biomedical document classification.
  • This multimodal approach offers a more comprehensive and accurate method for identifying relevant literature compared to text-only methods.
  • The developed scheme expedites the biocuration process, thereby supporting and accelerating biomedical research.