000002256 001__ 2256
000002256 005__ 20181220113735.0
000002256 020__ $$a978-0-12-812133-7
000002256 0247_ $$2DOI$$a10.1016/B978-0-12-812133-7.00010-7
000002256 037__ $$aCHAPTER
000002256 041__ $$aeng
000002256 245__ $$bfrom traditional machine learning to deep learning$$aAnalysis of histopathology images :
000002256 260__ $$c2017$$bElsevier$$a[S. l.]
000002256 269__ $$a2017-09
000002256 300__ $$app.  281–314
000002256 506__ $$avisible
000002256 520__ $$aDigitizing pathology is a current trend that makes large amounts of visual data available for automatic analysis. It allows to visualize and interpret pathologic cell and tissue samples in high-resolution images and with the help of computer tools. This opens the possibility to develop image analysis methods that help pathologists and support their image descriptions (i.e., staging, grading) with objective quantification of image features. Numerous detection, classification and segmentation algorithms of the underlying tissue primitives in histopathology images have been proposed in this respect. To better select the most suitable algorithms for histopathology tasks, biomedical image analysis challenges have evaluated and compared both traditional feature extraction with machine learning and deep learning techniques. This chapter provides an overview of methods addressing the analysis of histopathology images, as well as a brief description of the tasks they aim to solve. It is focused on histopathology images containing textured areas of different types.$$9eng
000002256 546__ $$aEnglish
000002256 540__ $$acorrect
000002256 592__ $$aHEG-VS
000002256 592__ $$bInstitut Informatique de gestion
000002256 592__ $$cEconomie et Services
000002256 65017 $$aInformatique
000002256 6531_ $$ahistopathology$$9eng
000002256 6531_ $$adeep learning$$9eng
000002256 6531_ $$abiomedical texture analysis$$9eng
000002256 6531_ $$adigital pathology$$9eng
000002256 700__ $$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University of Geneva, Switzerland$$aJimenez-del-Toro, Oscar
000002256 700__ $$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)$$aOtálora, Sebastian
000002256 700__ $$uContextVision, Stockholm, Sweden$$aAndersson, Mats
000002256 700__ $$uContextVision, Stockholm, Sweden$$aEurén, Kristian
000002256 700__ $$uContextVision, Stockholm, Sweden$$aHedlund, Martin
000002256 700__ $$uContextVision, Stockholm, Sweden$$aRousson, Mikael
000002256 700__ $$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University of Geneva, Switzerland$$aMüller, Henning
000002256 700__ $$aAtzori, Manfredo$$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)
000002256 773__ $$tBiomedical texture analysis : fundamentals, tools and challenges
000002256 85641 $$uhttps://www.swissbib.ch/Record/498692337$$zLien vers le catalogue des bibliothèques
000002256 8564_ $$uhttps://hesso.tind.io/record/2256/files/DelToro_2017_analysis_histopathology.pdf$$s2519916
000002256 8564_ $$uhttps://hesso.tind.io/record/2256/files/DelToro_2017_analysis_histopathology.pdf?subformat=pdfa$$s4646769$$xpdfa
000002256 909CO $$pGLOBAL_SET$$ooai:hesso.tind.io:2256
000002256 906__ $$aGREEN
000002256 950__ $$aI2
000002256 980__ $$achapitre