Medical Content-Based Retrieval for Clinical Decision by Adrien Depeursinge, Hayit Greenspan, Tanveer Syeda, Henning

Medical Content-Based Retrieval for Clinical Decision by Adrien Depeursinge, Hayit Greenspan, Tanveer Syeda, Henning

By Adrien Depeursinge, Hayit Greenspan, Tanveer Syeda, Henning Müller (auth.), Henning Müller, Hayit Greenspan, Tanveer Syeda-Mahmood (eds.)

This booklet constitutes the refereed lawsuits of the second one MICCAI Workshop on clinical Content-Based Retrieval for medical determination aid, MCBR-CBS 2011, held in Toronto, Canada, in September 2011. The eleven revised complete papers awarded including 2 invited talks have been rigorously reviewed and chosen from 17 submissions. The papers are divided on numerous issues on scientific snapshot retrieval with textual methods, visible note established methods, purposes and multidimensional retrieval.

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Additional info for Medical Content-Based Retrieval for Clinical Decision Support: Second MICCAI International Workshop, MCBR-CDS 2011, Toronto, ON, Canada, September 22, 2011, Revised Selected Papers

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LNCS, vol. 6893, pp. 297–304. Springer, Heidelberg (2011) 12. : An Image Retrieval Approach to Setup Difficulty Levels in Training Systems for Endomicroscopy Diagnosis. A. ) MICCAI 2010. LNCS, vol. 6362, pp. 480–487. Springer, Heidelberg (2010) 13. : Confocal laser endoscopy for diagnosing intraepithelial neoplasias and colorectal cancer in vivo. Gastroenterology 127(3), 706–713 (2004) 14. : Bridging the gap: Query by semantic example. IEEE Transactions on Multimedia 9(5), 923–938 (2007) 15. : Learning Pit Pattern Concepts for Gastroenterological Training.

For the survey and experimentation we employed the collections used in the medical image retrieval task (ImageCLEF4) in 2009 [5] and 2010 [6]. In these collections, the information linked to each image (metadata) consists of the image caption and the title of the article to which it belongs. It also includes the ID of the article to access the full text. The rest of the paper is organised as follows. Section 2 describes the most relevant related work. Section 3 presents the collection of documents used in the survey and experimentation.

7 on the right. We demonstrate that, in terms of correlation with the perceived similarity, the distance learning method allows to improve with statistical significance, the correlation with the perceived similarity. For consistency checking, we also test the distance learning method on the semantic signatures, and we show that the transformed semantics signature provides a significantly higher correlation with the perceived similarity. 6 Conclusion In this paper, we presented the first CBIR framework for training and diagnosis support in the field of in vivo endomicroscopy.

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