000 03892nam a22006015i 4500
001 978-3-030-40124-5
003 DE-He213
005 20240730171044.0
007 cr nn 008mamaa
008 200224s2020 sz | s |||| 0|eng d
020 _a9783030401245
_9978-3-030-40124-5
024 7 _a10.1007/978-3-030-40124-5
_2doi
050 4 _aTA1634
072 7 _aUYQV
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYQV
_2thema
082 0 4 _a006.37
_223
245 1 0 _aRadiomics and Radiogenomics in Neuro-oncology
_h[electronic resource] :
_bFirst International Workshop, RNO-AI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings /
_cedited by Hassan Mohy-ud-Din, Saima Rathore.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aIX, 91 p. 22 illus., 19 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v11991
505 0 _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. .
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. .
650 0 _aComputer vision.
_996911
650 0 _aMachine learning.
_91831
650 0 _aComputer networks .
_931572
650 0 _aApplication software.
_996913
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputer Vision.
_996914
650 2 4 _aMachine Learning.
_91831
650 2 4 _aComputer Communication Networks.
_996916
650 2 4 _aComputer and Information Systems Applications.
_996918
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aMohy-ud-Din, Hassan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_996921
700 1 _aRathore, Saima.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_996922
710 2 _aSpringerLink (Online service)
_996925
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030401238
776 0 8 _iPrinted edition:
_z9783030401252
830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v11991
_996926
856 4 0 _uhttps://doi.org/10.1007/978-3-030-40124-5
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cELN
999 _c87378
_d87378