Automated feedback extraction for medical imaging retrieval

Weidong Cai, Fan Zhang, Yang Song, Sidong Liu, Lingfeng Wen, Stefan Eberl, Michael Fulham, Dagan Feng

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

7 Citations (Scopus)


Content-based image retrieval (CBIR) has been widely used in many medical applications by providing objective depictions and the initial screening to facilitate the manual interpretations by the radiologists. To achieve accurate retrieval results, relevance feedback is usually incorporated into CBIR to refine the retrieved items, but its effectiveness is restricted by the huge number of medical cases. Therefore, in this study we propose an automated feedback extraction method to exclude the involvement of radiologists. Instead of incorporating the feedbacks from them, the similarity relationship between the initial retrieval results and all candidate images is used to indicate the preferences of these retrieved items regarding to the query, i.e., relevance or irrelevance, and to further re-rank the candidates. The experimental results on a publicly available brain image dataset for neurodegenerative disorder diagnosis demonstrate the promising retrieval performance of the proposed method.

Original languageEnglish
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)9781467319591, 9781467319614
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: 29 Apr 20142 May 2014


Conference2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014


  • Automated relevance feedback
  • CBIR
  • Medical imaging


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