Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
User performance versus precision measures for simple search tasks
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
International Journal of Computer Vision
A statistical approach for image difficulty estimation in x-ray screening using image measurements
Proceedings of the 4th symposium on Applied perception in graphics and visualization
A survey of pre-retrieval query performance predictors
Proceedings of the 17th ACM conference on Information and knowledge management
A case for improved evaluation of query difficulty prediction
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Effective pre-retrieval query performance prediction using similarity and variability evidence
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Endomicroscopic video retrieval using mosaicing and visual words
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Content-Based retrieval in endomicroscopy: toward an efficient smart atlas for clinical diagnosis
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
A polynomial model of surgical gestures for real-time retrieval of surgery videos
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Learning medical image interpretation is an evolutive process that requires modular training systems, from non-expert to expert users. Our study aims at developing such a system for endomicroscopy diagnosis. It uses a difficulty predictor to try and shorten the physician learning curve. As the understanding of video diagnosis is driven by visual similarities, we propose a content-based video retrieval approach to estimate the level of interpretation difficulty. The performance of our retrieval method is compared with several state of the art methods, and its genericity is demonstrated with two different clinical databases, on the Barrett's Esophagus and on colonic polyps. From our retrieval results, we learn a difficulty predictor against a ground truth given by the percentage of false diagnoses among several physicians. Our experiments show that, although our datasets are not large enough to test for statistical significance, there is a noticeable relationship between our retrieval-based difficulty estimation and the difficulty experienced by the physicians.