000002125 001__ 2125
000002125 005__ 20181220113731.0
000002125 020__ $$a978-3-319-49642-9
000002125 0247_ $$2DOI$$a10.1007/978-3-319-49644-3_13
000002125 037__ $$aCHAPTER
000002125 041__ $$aeng
000002125 245__ $$aCombining radiology images and clinical metadata for multimodal medical case-based retrieval
000002125 260__ $$c2017$$bSpringer$$aCham
000002125 269__ $$a2017-05
000002125 300__ $$app. 221-236
000002125 506__ $$avisible
000002125 520__ $$aAs part of their daily workload, clinicians examine patient cases in the process of formulating a diagnosis. These large multimodal patient datasets stored in hospitals could help in retrieving relevant information for a differential diagnosis, but these are currently not fully exploited. The VISCERAL Retrieval Benchmark organized a medical case-based retrieval algorithm evaluation using multimodal (text and visual) data from radiology reports. The common dataset contained patient CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) scans and RadLex term anatomy–pathology lists from the radiology reports. A content-based retrieval method for medical cases that uses both textual and visual features is presented. It defines a weighting scheme that combines the anatomical and clinical correlations of the RadLex terms with local texture features obtained from the region of interest in the query cases. The visual features are computed using a 3D Riesz wavelet texture analysis performed on a common spatial domain to compare the images in the analogous anatomical regions of interest in the dataset images. The proposed method obtained the best mean average precision in 6 out of 10 topics and the highest number of relevant cases retrieved in the benchmark. Obtaining robust results for various pathologies, it could further be developed to perform medical case-based retrieval on large multimodal clinical datasets.$$9eng
000002125 546__ $$aEnglish
000002125 540__ $$acorrect
000002125 592__ $$aHEG-VS
000002125 592__ $$bInstitut Informatique de gestion
000002125 592__ $$cEconomie et Services
000002125 65017 $$aEconomie/gestion
000002125 700__ $$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)$$aJimenez-del-Toro, Oscar
000002125 700__ $$uDepartment of Information and Communication TechnologiesUniversitat Pompeu Fabra, Barcelona, Spain$$aCirujeda, Pol
000002125 700__ $$aMüller, Henning$$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University Hospitals of Geneva, Switzerland
000002125 773__ $$tCloud-based benchmarking of medical image analysis
000002125 85641 $$uhttps://www.swissbib.ch/Record/492208352$$zAccès au catalogue de bibliothèques
000002125 8564_ $$uhttps://hesso.tind.io/record/2125/files/Jimenez_del_toro_2017_combining_radiology_.pdf$$s2527962
000002125 8564_ $$xpdfa$$uhttps://hesso.tind.io/record/2125/files/Jimenez_del_toro_2017_combining_radiology_.pdf?subformat=pdfa$$s2525210
000002125 909CO $$pGLOBAL_SET$$ooai:hesso.tind.io:2125
000002125 906__ $$aGREEN
000002125 950__ $$aI2
000002125 980__ $$achapitre