語系:
繁體中文
English
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
Artificial intelligence in radiation...
~
AIRT (Workshop) (2019 :)
FindBook
Google Book
Amazon
博客來
Artificial intelligence in radiation therapy = first International Workshop, AIRT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Artificial intelligence in radiation therapy/ edited by Dan Nguyen, Lei Xing, Steve Jiang.
其他題名:
first International Workshop, AIRT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
其他題名:
AIRT 2019
其他作者:
Nguyen, Dan.
團體作者:
AIRT (Workshop)
出版者:
Cham :Springer International Publishing : : 2019.,
面頁冊數:
xi, 172 p. :ill. (some col.), digital ;24 cm.
內容註:
Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Radiation Therapy -- Feasibility of CT-only 3D dose prediction for VMAT prostate plans using deep learning -- Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiation Treatment Using Image Saliency -- 4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network -- Toward markerless image-guided radiotherapy using deep learning for prostate cancer -- A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network -- A Novel Deep Learning Framework for Standardizing the Label of OARs in CT -- Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery -- Voxel-level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions -- Online Target Volume Estimation and Prediction From an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach -- One-dimensional convolutional network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning -- Unpaired Synthetic Image Generation in Radiology Using GANs -- Deriving lung perfusion directly from CT image using deep convolutional neural network: A preliminary study -- Individualized 3D Dose Distribution Prediction Using Deep Learning -- Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy -- Dose Distribution Prediction for Optimal Treatment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma -- DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy -- UC-GAN for MR to CT Image Synthesis -- CBCT-based Synthetic MRI Generation for CBCT-guided Adaptive Radiotherapy -- Cardio-pulmonary Substructure Segmentation of CT images using Convolutional Neural Networks.
Contained By:
Springer eBooks
標題:
Artificial intelligence - Medical applications -
電子資源:
https://doi.org/10.1007/978-3-030-32486-5
ISBN:
9783030324865
Artificial intelligence in radiation therapy = first International Workshop, AIRT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
Artificial intelligence in radiation therapy
first International Workshop, AIRT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /[electronic resource] :AIRT 2019edited by Dan Nguyen, Lei Xing, Steve Jiang. - Cham :Springer International Publishing :2019. - xi, 172 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,118500302-9743 ;. - Lecture notes in computer science ;11850..
Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Radiation Therapy -- Feasibility of CT-only 3D dose prediction for VMAT prostate plans using deep learning -- Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiation Treatment Using Image Saliency -- 4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network -- Toward markerless image-guided radiotherapy using deep learning for prostate cancer -- A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network -- A Novel Deep Learning Framework for Standardizing the Label of OARs in CT -- Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery -- Voxel-level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions -- Online Target Volume Estimation and Prediction From an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach -- One-dimensional convolutional network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning -- Unpaired Synthetic Image Generation in Radiology Using GANs -- Deriving lung perfusion directly from CT image using deep convolutional neural network: A preliminary study -- Individualized 3D Dose Distribution Prediction Using Deep Learning -- Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy -- Dose Distribution Prediction for Optimal Treatment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma -- DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy -- UC-GAN for MR to CT Image Synthesis -- CBCT-based Synthetic MRI Generation for CBCT-guided Adaptive Radiotherapy -- Cardio-pulmonary Substructure Segmentation of CT images using Convolutional Neural Networks.
This book constitutes the refereed proceedings of the First International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 20 full papers presented were carefully reviewed and selected from 24 submissions. The papers discuss the state of radiation therapy, the state of AI and related technologies, and hope to find a pathway to revolutionizing the field to ultimately improve cancer patient outcome and quality of life.
ISBN: 9783030324865
Standard No.: 10.1007/978-3-030-32486-5doiSubjects--Topical Terms:
660945
Artificial intelligence
--Medical applications
LC Class. No.: R859.7.A78 / A57 2019
Dewey Class. No.: 610.28563
Artificial intelligence in radiation therapy = first International Workshop, AIRT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
LDR
:03887nmm a2200385 a 4500
001
2218843
003
DE-He213
005
20191028132057.0
006
m d
007
cr nn 008maaau
008
201126s2019 sz s 0 eng d
020
$a
9783030324865
$q
(electronic bk.)
020
$a
9783030324858
$q
(paper)
024
7
$a
10.1007/978-3-030-32486-5
$2
doi
035
$a
978-3-030-32486-5
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
R859.7.A78
$b
A57 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
A298 2019
111
2
$a
AIRT (Workshop)
$n
(1st :
$d
2019 :
$c
Shenzhen Shi, China)
$3
3453598
245
1 0
$a
Artificial intelligence in radiation therapy
$h
[electronic resource] :
$b
first International Workshop, AIRT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
$c
edited by Dan Nguyen, Lei Xing, Steve Jiang.
246
3
$a
AIRT 2019
246
3
$a
MICCAI 2019
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2019.
300
$a
xi, 172 p. :
$b
ill. (some col.), digital ;
$c
24 cm.
490
1
$a
Lecture notes in computer science,
$x
0302-9743 ;
$v
11850
490
1
$a
Image processing, computer vision, pattern recognition, and graphics
505
0
$a
Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Radiation Therapy -- Feasibility of CT-only 3D dose prediction for VMAT prostate plans using deep learning -- Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiation Treatment Using Image Saliency -- 4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network -- Toward markerless image-guided radiotherapy using deep learning for prostate cancer -- A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network -- A Novel Deep Learning Framework for Standardizing the Label of OARs in CT -- Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery -- Voxel-level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions -- Online Target Volume Estimation and Prediction From an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach -- One-dimensional convolutional network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning -- Unpaired Synthetic Image Generation in Radiology Using GANs -- Deriving lung perfusion directly from CT image using deep convolutional neural network: A preliminary study -- Individualized 3D Dose Distribution Prediction Using Deep Learning -- Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy -- Dose Distribution Prediction for Optimal Treatment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma -- DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy -- UC-GAN for MR to CT Image Synthesis -- CBCT-based Synthetic MRI Generation for CBCT-guided Adaptive Radiotherapy -- Cardio-pulmonary Substructure Segmentation of CT images using Convolutional Neural Networks.
520
$a
This book constitutes the refereed proceedings of the First International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 20 full papers presented were carefully reviewed and selected from 24 submissions. The papers discuss the state of radiation therapy, the state of AI and related technologies, and hope to find a pathway to revolutionizing the field to ultimately improve cancer patient outcome and quality of life.
650
0
$a
Artificial intelligence
$x
Medical applications
$x
Congresses.
$3
660945
650
1 4
$a
Image Processing and Computer Vision.
$3
891070
650
2 4
$a
Artificial Intelligence.
$3
769149
650
2 4
$a
Health Informatics.
$3
892928
700
1
$a
Nguyen, Dan.
$3
3176716
700
1
$a
Xing, Lei.
$3
1360428
700
1
$a
Jiang, Steve.
$3
3453599
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
11850.
$3
3453600
830
0
$a
Image processing, computer vision, pattern recognition, and graphics.
$3
3382509
856
4 0
$u
https://doi.org/10.1007/978-3-030-32486-5
950
$a
Computer Science (Springer-11645)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9393702
電子資源
11.線上閱覽_V
電子書
EB R859.7.A78 A57 2019
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入