By Henning Müller, Oscar Alfonso Jimenez del Toro, Allan Hanbury, Georg Langs, Antonio Foncubierta Rodriguez
This publication constitutes the court cases of the 1st foreign Workshop on Multimodal Retrieval within the scientific area, MRMD 2015, held in Vienna, Austria, on March 29, 2015. The workshop was once held in reference to ECIR 2015.
The 14 complete papers awarded, together with one invited paper, a workshop evaluation and 5 papers at the VISCERAL Retrieval Benchmark, have been rigorously reviewed and chosen from 18 submissions. The papers specialize in the subsequent issues: significance of knowledge except textual content for info retrieval; semantic info research; scalability ways in the direction of mammoth information sets.
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Extra resources for Multimodal Retrieval in the Medical Domain: First International Workshop, MRMD 2015, Vienna, Austria, March 29, 2015, Revised Selected Papers
Particularly, EHR repositories enable to compose patient cohorts for the study of clinical hypotheses, hard to test experimentally, such as for example individual variability in drug responses. However, to compose those cohorts, efﬁcient and user-friendly Information Retrieval systems are needed. © Springer International Publishing Switzerland 2015 H. Müller et al. ): MRMD 2015, LNCS 9059, pp. 40–53, 2015. 1007/978-3-319-24471-6_4 Rewriting Natural Language Queries Using Patterns 41 In fact, EHR systems are often complex and Information Retrieval (IR) through these systems often requires a good knowledge of both the data structure and the dedicated Query Language associated to the underlying system.
Imaging 25, 708–719 (2012) 5. : Learning semantic and visual similarity for endomicroscopy video retrieval. IEEE Trans. Med. Imaging 31(6), 1276–1288 (2012) 6. : Capturing image semantics with low-level descriptors. In: IEEE ICIP, pp. 18–21 (2001) 7. : ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009) 8. : Automated retrieval of CT images of liver lesions on the basis of image similarity: Method and preliminary results. Radiology 256(1), 243–252 (2010) Semantic Retrieval of Radiological Images with Relevance Feedback 25 9.
Systems achieved mediocre results but are much faster in inference time than ﬂat ones making them appealing for large-scale problems. Task B. Figure 4 presents the average performances of the systems for the two editions of the challenge for documents, concepts and ideal answers. Clearly, the systems submitted in the second year of the challenge exhibit a noticeable improvement of their counterparts in the ﬁrst edition. Additionally, the ideal answers which were assesed by the BioASQmedical expert team achieved high scores showing that the systems were able to provide high quality answers.