Jimenez-del-Toro, OscarCirujeda Santolaria, PolMüller, Henning2025-05-272025-05-272017Jimenez-del-Toro O, Cirujeda P, Müller H. Combining radiology images and clinical metadata for multimodal medical case-based retrieval. In: Hanbury A, Müller H, Langs G, editors. Cloud-based benchmarking of medical Image analysis. Cham: Springer; c2017. p. 221-36. DOI: 10.1007/978-3-319-49644-3_139783319496429http://hdl.handle.net/10230/70507As part of their daily workload, clinicians examine patient cases in the process of formulating a diagnosis. These large multimodal patient datasets stored in hospitals could help in retrieving relevant information for a differential diagnosis , but these are currently not fully exploited. The VISCERAL Retrieval Benchmark organized a medical case-based retrieval algorithm evaluation using multimodal (text and visual) data from radiology reports. The common dataset contained patient CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) scans and RadLex term anatomy–pathology lists from the radiology reports. A content-based retrieval method for medical cases that uses both textual and visual features is presented. It defines a weighting scheme that combines the anatomical and clinical correlations of the RadLex terms with local texture features obtained from the region of interest in the query cases. The visual features are computed using a 3D Riesz wavelet texture analysis performed on a common spatial domain to compare the images in the analogous anatomical regions of interest in the dataset images. The proposed method obtained the best mean average precision in 6 out of 10 topics and the highest number of relevant cases retrieved in the benchmark. Obtaining robust results for various pathologies, it could further be developed to perform medical case-based retrieval on large multimodal clinical datasets.application/pdfengThis chapter is distributed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the work’s Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work’s Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.info:eu-repo/semantics/bookParthttp://dx.doi.org/10.1007/978-3-319-49644-3_13Query imageRadiology reportMean average precisionDescriptor spaceCovariance descriptorinfo:eu-repo/semantics/openAccess