Abstract
The results of the VISCERAL 3D case retrieval benchmark were presented during the Multimodal Retrieval in the Medical Domain (MRMD) 2015 workshop in Vienna, Austria on March 29, 2015. The main task for the participanta was to find and rank similar medical cases from a large multimodal (semantic RadLex terms extracted from text and visual 3D data) data set using a query case as input. The approaches that integrated information from both the RadLex terms and the 3D volumes provided in the benchmark obtained the best results based on 5 standard evaluation metrics. The benchmark set up, data set description and result analysis from the benchmark are presented for all the submitted methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
http://www.visceral.eu/benchmarks/retrieval-benchmark/, as of 1st may 2015.
- 2.
http://www.crowdflower.com/, as of 1st May 2015.
- 3.
http://trec.nist.gov/trec_eval/, as of 1st May 2015.
References
Choi, S.: Multimodal Medical Case-Based Retrieval on the Image and Report: SNUMedinfo at VISCERAL Benchmark. In: Müller, H., et al. (eds.) multimodal retrieval in the medical domain. LNCS, vol. 9059, pp. 124–128. Springer, Heidelberg (2015)
Foncubierta-Rodríguez, A., Müller, H.: Ground truth generation in medical imaging: a crowdsourcing based iterative approach. In: Workshop on Crowdsourcing for Multimedia, ACM Multimedia, October 2012
García Seco de Herrera, A.: Use case oriented medical visual information retrieval & system evaluation. Ph.D. thesis, University of Geneva (2015)
García Seco de Herrera, A., Foncubierta-Rodríguez, A., Markonis, D., Schaer, R., Müller, H.: Crowdsourcing for medical image classification. In: Annual Congress SGMI 2014 (2014)
Jiménez-del-Toro, O.A., Cirujeda, P., Dicente Cid, Y., Müller, H.: RadLex terms and local texture features for multimodal medical case retrival. In: Müller, H., et al. (eds.) Multimodal Retrieval in the Medical Domain. LNCS, vol. 9059, pp. 144–152. Springer, Heidelberg (2015)
Langlotz, C.P.: Radlex: A new method for indexing online educational materials. Radiographics 26(6), 1595–1597 (2006)
Quellec, G., Lamard, M., Bekri, L., Cazuguel, G., Roux, C., Cochener, B.: Medical case retrieval from a committee of decision trees. IEEE Trans. Inf. Technol. Biomed. 14(5), 1227–1235 (2010)
Spanier, A.B., Joskowicz, L.: Medical Case-Based Retrieval of Patient Records Using the Radlex Hierarchical Lexicon. In: Müller, H., et al. (eds.) Multimodal Retrieval in the Medical Domain. LNCS, vol. 9059, pp. 129–138. Springer, Heidelberg (2015)
Zhang, F., Song, Y., Cai, W., Depeursinge, A., Müller, H.:USYD/HES–SO in the VISCERAL Retrieval Benchmark. In: Müller, H., et al. (eds.) Multimodal Retrieval in the Medical Domain. LNCS, vol. 9059, pp. 139–143. Springer, Heidelberg (2015)
Acknowledgments
This research was funded by the EU via the FP7 VISCERAL project (318068).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Jiménez–del–Toro, O.A., Hanbury, A., Langs, G., Foncubierta–Rodríguez, A., Müller, H. (2015). Overview of the VISCERAL Retrieval Benchmark 2015. In: Müller, H., Jimenez del Toro, O., Hanbury, A., Langs, G., Foncubierta Rodriguez, A. (eds) Multimodal Retrieval in the Medical Domain. MRDM 2015. Lecture Notes in Computer Science(), vol 9059. Springer, Cham. https://doi.org/10.1007/978-3-319-24471-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-24471-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24470-9
Online ISBN: 978-3-319-24471-6
eBook Packages: Computer ScienceComputer Science (R0)