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Open Access 2017 | OriginalPaper | Buchkapitel

Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark

verfasst von : Oscar Jimenez-del-Toro, Henning Müller, Antonio Foncubierta-Rodriguez, Georg Langs, Allan Hanbury

Erschienen in: Cloud-Based Benchmarking of Medical Image Analysis

Verlag: Springer International Publishing

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Health providers currently construct their differential diagnosis for a given medical case most often based on textbook knowledge and clinical experience. Data mining of the large amount of medical records generated daily in hospitals is only very rarely done, limiting the reusability of these cases. As part of the VISCERAL project, the Retrieval benchmark was organized to evaluate available approaches for medical case-based retrievalCase-based retrieval. Participant algorithms were required to find and rank relevant medical cases from a large multimodal dataset (including semantic RadLex terms extracted from text and visual 3D data) for common query topics. The relevance assessment of the cases was done by medical experts who selected cases that are useful for a differential diagnosis for the given query case. The approaches that integrated information from both the RadLex terms and the 3D volumes (mixed techniques) obtained the best results based on five standard evaluation metrics. The benchmark set up, dataset description and result analysis are presented.

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Metadaten
Titel
Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark
verfasst von
Oscar Jimenez-del-Toro
Henning Müller
Antonio Foncubierta-Rodriguez
Georg Langs
Allan Hanbury
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-49644-3_8