AI Article Synopsis
- Biomedical information extraction systems struggle to interpret image content without accompanying text, prompting the development of a visual ontology for better retrieval of biomedical images.
- This visual ontology connects image regions to existing textual concepts, enhancing the understanding of visual characteristics and their meanings.
- We automated the creation of this ontology by linking image regions to descriptions and demonstrated its efficacy through a method that classifies image regions based on their appearance, showing promise in thoracic imaging classification.
Article Abstract
Image content is frequently the target of biomedical information extraction systems. However, the meaning of this content cannot be easily understood without some associated text. In order to improve the integration of textual and visual information, we are developing a visual ontology for biomedical image retrieval. Our visual ontology maps the appearance of image regions to concepts in an existing textual ontology, thereby inheriting relationships among the visual entities. Such a resource creates a bridge between the visual characteristics of important image regions and their semantic interpretation. We automatically populate our visual ontology by pairing image regions with their associated descriptions. To demonstrate the usefulness of this resource, we have developed a classification method that automatically labels image regions with appropriate concepts based solely on their appearance. Our results for thoracic imaging terms show that our methods are promising first steps towards the creation of a biomedical visual ontology.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540530 | PMC |
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