Manual query modification and data fusion for medical image retrieval

Authors:
Jeffery R. Jensen;William R. Hersh
Affiliations:
Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon;Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
Venue:
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Year:
2005

Citing 1
Cited 2

The effect multiple query representations on information retrieval system performance

SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval

Editorial: Information fusion in the realm of medical applications - A bibliographic glimpse at its growing appeal

Information Fusion
Medical image retrieval and automated annotation: OHSU at ImageCLEF 2006

CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

Image retrieval has great potential for a variety of tasks in medicine but is currently underdeveloped. For the ImageCLEF 2005 medical task, we used a text retrieval system as the foundation of our experiments to assess retrieval of images from the test collection. We conducted experiments using automatic queries, manual queries, and manual queries augmented with results from visual queries. The best performance was obtained from manual modification of queries. The combination of manual and visual retrieval results resulted in lower performance based on mean average precision but higher precision within the top 30 results. Further research is needed not only to sort out the relative benefit of textual and visual methods in image retrieval but also to determine which performance measures are most relevant to the operational setting.