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000929636 020__ $$a9783030401245
000929636 020__ $$a3030401243
000929636 0247_ $$a10.1007/978-3-030-40$$2doi
000929636 035__ $$aSP(OCoLC)on1142303958
000929636 035__ $$aSP(OCoLC)1142303958
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000929636 1112_ $$aRadiomics and Radiogenomics in Neuro-oncology using AI Workshop$$n(1st :$$d2019 :$$cShenzhen Shi, China)
000929636 24510 $$aRadiomics and radiogenomics in neuro-oncology:$$bFirst International Workshop, RNO-AI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, proceedings /$$cHassan Mohy-ud-Din, Saima Rathore (eds.).
000929636 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2020]
000929636 300__ $$a1 online resource (ix, 91 pages) :$$billustrations.
000929636 336__ $$atext$$btxt$$2rdacontent
000929636 337__ $$acomputer$$bc$$2rdamedia
000929636 338__ $$aonline resource$$bcr$$2rdacarrier
000929636 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v11991
000929636 4901_ $$aImage processing, computer vision, pattern recognition, and graphics
000929636 504__ $$aIncludes bibliographical references and index.
000929636 5050_ $$aCurrent Status of the Use of Machine Learning and Magnetic Resonance Imaging in the Field of Neuro- Radiomics -- Opportunities and Advances in Radiomics and Radiogenomics in Neuro-Oncology -- A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology -- Multimodal MRI for Radiogenomic Analysis of PTEN Mutation in Glioblastoma -- Deep radiomic features from MRI scans predict survival outcome of recurrent glio-blastoma -- cuRadiomics: A GPU-based Radiomics Feature Extraction Toolkit -- On validating multimodal MRI based stratification of IDH genotype in high grade gliomas using CNNs and its comparison to radiomics -- Imaging signature of 1p/19q co-deletion status derived via machine learning in lower grade glioma -- A feature-pooling and signature-pooling method for feature selection for quantitative image analysis: application to a radiomics model for survival in glioma -- Radiomics-Enhanced Multi-Task Neural Network for Non-invasive Glioma Subtyp-ing and Segmentation.
000929636 506__ $$aAccess limited to authorized users.
000929636 520__ $$aThis book constitutes the proceedings of the First International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, which was held in conjunction with MICCAI in Shenzhen, China, in October 2019. The 10 full papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the development of tools that can automate the analysis and synthesis of neuro-oncologic imaging.
000929636 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 26, 2020).
000929636 650_0 $$aArtificial intelligence$$xMedical applications$$vCongresses.
000929636 650_0 $$aDiagnostic imaging$$vCongresses.
000929636 650_0 $$aCancer$$xTreatment$$xTechnological innovations$$vCongresses.
000929636 7001_ $$aMohy-ud-Din, Hassan.
000929636 7001_ $$aRathore, Saima.
000929636 830_0 $$aLecture notes in computer science ;$$v11991.
000929636 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
000929636 852__ $$bebk
000929636 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-40124-5$$zOnline Access$$91397441.1
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000929636 980__ $$aEBOOK
000929636 980__ $$aBIB
000929636 982__ $$aEbook
000929636 983__ $$aOnline
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