An image retrieval approach to setup difficulty levels in training systems for endomicroscopy diagnosis

Barbara André, Tom Vercauteren, Anna M. Buchner, Muhammad Waseem Shahid, Michael B. Wallace, Nicholas Ayache

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages480-487
Number of pages8
Volume6362 LNCS
EditionPART 2
DOIs
StatePublished - 2010
Event13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 - Beijing, China
Duration: Sep 20 2010Sep 24 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6362 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
CountryChina
CityBeijing
Period9/20/109/24/10

Fingerprint

Image retrieval
Image Retrieval
Retrieval
Predictors
Video Retrieval
Learning Curve
Content-based Retrieval
Genericity
Statistical Significance
Medical Image
Percentage
Estimate
Experiment
Training
Experiments
Interpretation

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

André, B., Vercauteren, T., Buchner, A. M., Shahid, M. W., Wallace, M. B., & Ayache, N. (2010). An image retrieval approach to setup difficulty levels in training systems for endomicroscopy diagnosis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 6362 LNCS, pp. 480-487). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6362 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-15745-5_59

An image retrieval approach to setup difficulty levels in training systems for endomicroscopy diagnosis. / André, Barbara; Vercauteren, Tom; Buchner, Anna M.; Shahid, Muhammad Waseem; Wallace, Michael B.; Ayache, Nicholas.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6362 LNCS PART 2. ed. 2010. p. 480-487 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6362 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

André, B, Vercauteren, T, Buchner, AM, Shahid, MW, Wallace, MB & Ayache, N 2010, An image retrieval approach to setup difficulty levels in training systems for endomicroscopy diagnosis. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 6362 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6362 LNCS, pp. 480-487, 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010, Beijing, China, 9/20/10. https://doi.org/10.1007/978-3-642-15745-5_59
André B, Vercauteren T, Buchner AM, Shahid MW, Wallace MB, Ayache N. An image retrieval approach to setup difficulty levels in training systems for endomicroscopy diagnosis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 6362 LNCS. 2010. p. 480-487. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-15745-5_59
André, Barbara ; Vercauteren, Tom ; Buchner, Anna M. ; Shahid, Muhammad Waseem ; Wallace, Michael B. ; Ayache, Nicholas. / An image retrieval approach to setup difficulty levels in training systems for endomicroscopy diagnosis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6362 LNCS PART 2. ed. 2010. pp. 480-487 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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