東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Radiomics and radiogenomics in neuro...

Radiomics and Radiogenomics in Neuro-oncology using AI Workshop (2019 :)

FindBook

Google Book

Amazon

博客來

Radiomics and radiogenomics in neuro-oncology = first International Workshop, RNO-AI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Radiomics and radiogenomics in neuro-oncology/ edited by Hassan Mohy-ud-Din, Saima Rathore.
其他題名:	first International Workshop, RNO-AI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13 : proceedings /
其他題名:	RNO-AI 2019
其他作者:	Mohy-ud-Din, Hassan.
團體作者:	Radiomics and Radiogenomics in Neuro-oncology using AI Workshop
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	ix, 91 p. :ill. (some col.), digital ;24 cm.
內容註:	Current 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.
Contained By:	Springer eBooks
標題:	Artificial intelligence - Medical applications -
電子資源:	https://doi.org/10.1007/978-3-030-40124-5
ISBN:	9783030401245

Radiomics and radiogenomics in neuro-oncology = first International Workshop, RNO-AI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13 : proceedings /
Radiomics and radiogenomics in neuro-oncologyfirst International Workshop, RNO-AI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13 : proceedings /[electronic resource] :RNO-AI 2019edited by Hassan Mohy-ud-Din, Saima Rathore. - Cham :Springer International Publishing :2020. - ix, 91 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,119910302-9743 ;. - Lecture notes in computer science ;11991..

Current 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.

This 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.

ISBN: 9783030401245

Standard No.: 10.1007/978-3-030-40124-5doiSubjects--Topical Terms:

660945
Artificial intelligence
--Medical applications

LC Class. No.: R859.7.A78 / R33 2019

Dewey Class. No.: 610.28563

Radiomics and radiogenomics in neuro-oncology = first International Workshop, RNO-AI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13 : proceedings /
LDR:02789nmm a2200385 a 4500 001 2216118
003 DE-He213
005 20200224164753.0
006 m d
007 cr nn 008maaau
008 201120s2020 sz s 0 eng d
020 $a 9783030401245 $q (electronic bk.)
020 $a 9783030401238 $q (paper)
024 7 $a 10.1007/978-3-030-40124-5 $2 doi
035 $a 978-3-030-40124-5
040 $a GP $c GP
041 0 $a eng
050 4 $a R859.7.A78 $b R33 2019
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 610.28563 $2 23
090 $a R859.7.A78 $b R129 2019
111 2 $a Radiomics and Radiogenomics in Neuro-oncology using AI Workshop $n (1st : $d 2019 : $c Shenzhen Shi, China) $3 3448184
245 1 0 $a Radiomics and radiogenomics in neuro-oncology $h [electronic resource] : $b first International Workshop, RNO-AI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13 : proceedings / $c edited by Hassan Mohy-ud-Din, Saima Rathore.
246 3 $a RNO-AI 2019
246 3 $a MICCAI 2019
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a ix, 91 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 11991
490 1 $a Image processing, computer vision, pattern recognition, and graphics
505 0 $a Current 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 $a This 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 $a Artificial intelligence $x Medical applications $x Congresses. $3 660945
650 0 $a Diagnostic imaging $v Congresses. $3 879904
650 0 $a Cancer $x Treatment $x Technological innovations $v Congresses. $3 3448188
650 1 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Information Systems and Communication Service. $3 891044
650 2 4 $a Computer Applications. $3 891249
650 2 4 $a Pattern Recognition. $3 891045
700 1 $a Mohy-ud-Din, Hassan. $3 3448185
700 1 $a Rathore, Saima. $3 3448186
710 2 $a SpringerLink (Online service) $3 836513
711 2 $a International Conference on Medical Image Computing and Computer-Assisted Intervention $n (22nd : $d 2019 : $c Shenzhen Shi, China) $3 3446146
773 0 $t Springer eBooks
830 0 $a Lecture notes in computer science ; $v 11991. $3 3448187
830 0 $a Image processing, computer vision, pattern recognition, and graphics. $3 3382509
856 4 0 $u https://doi.org/10.1007/978-3-030-40124-5
950 $a Computer Science (Springer-11645)