000002220 001__ 2220
000002220 005__ 20190211171338.0
000002220 020__ $$a978-3-319-67433-9
000002220 0247_ $$2DOI$$a10.1007/978-3-319-67434-6_17
000002220 037__ $$aCHAPTER
000002220 041__ $$aeng
000002220 245__ $$aDeep multimodal case–based retrieval for large histopathology datasets
000002220 260__ $$c2017$$bSpringer$$aCham
000002220 269__ $$a2017-09
000002220 300__ $$app. 149-157
000002220 506__ $$avisible
000002220 520__ $$9eng$$aThe current gold standard for interpreting patient tissue samples is the visual inspection of whole–slide histopathology images (WSIs) by pathologists. They generate a pathology report describing the main findings relevant for diagnosis and treatment planning. Search-ing for similar cases through repositories for differential diagnosis is often not done due to a lack of efficient strategies for medical case–based re-trieval. A patch–based multimodal retrieval strategy that retrieves sim-ilar pathology cases from a large data set fusing both visual and text information is explained in this paper. By fine–tuning a deep convolu-tional neural network an automatic representation is obtained for the vi-sual content of weakly annotated WSIs (using only a global cancer score and no manual annotations). The pathology text report is embedded into a category vector of the pathology terms also in a non–supervised approach. A publicly available data set of 267 prostate adenocarcinoma cases with their WSIs and corresponding pathology reports was used to train and evaluate each modality of the retrieval method. A MAP (Mean Average Precision) of 0.54 was obtained with the multimodal method in a previously unseen test set. The proposed retrieval system can help in differential diagnosis of tissue samples and during the training of pathol-ogists, exploiting the large amount of pathology data already existing digital hospital repositories.
000002220 546__ $$aEnglish
000002220 540__ $$acorrect
000002220 592__ $$aHEG-VS
000002220 592__ $$bInstitut Informatique de gestion
000002220 592__ $$cEconomie et Services
000002220 65017 $$aInformatique
000002220 700__ $$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University of Geneva (UNIGE) Switzerland$$aJimenez-del-Toro, Oscar
000002220 700__ $$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University of Geneva (UNIGE) Switzerland$$aOtálora, Sebastian
000002220 700__ $$uUniversity of Geneva (UNIGE) Switzerland$$aAtzori, Manfredo
000002220 700__ $$aMüller, Henning$$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University of Geneva (UNIGE) Switzerland
000002220 773__ $$tPatch-based techniques in medical imaging : third International Workshop, Patch-MI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings
000002220 85641 $$uhttps://www.swissbib.ch/Record/495913162$$zLien vers le catalogue des bibliothèques
000002220 8564_ $$uhttps://hesso.tind.io/record/2220/files/Jimenez_2017_deep_multimodal_case-based.pdf$$s3622219
000002220 8564_ $$xpdfa$$uhttps://hesso.tind.io/record/2220/files/Jimenez_2017_deep_multimodal_case-based.pdf?subformat=pdfa$$s3098455
000002220 909CO $$pGLOBAL_SET$$ooai:hesso.tind.io:2220
000002220 906__ $$aGREEN
000002220 950__ $$aI2
000002220 980__ $$achapitre