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
Uncertainty for safe utilization of ...
~
UNSURE (Workshop) (2019 :)
FindBook
Google Book
Amazon
博客來
Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures = first International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 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:
Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures/ edited by Hayit Greenspan ... [et al.].
其他題名:
first International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
其他題名:
UNSURE 2019
其他作者:
Greenspan, Hayit.
團體作者:
UNSURE (Workshop)
出版者:
Cham :Springer International Publishing : : 2019.,
面頁冊數:
xvii, 192 p. :ill. (some col.), digital ;24 cm.
內容註:
UNSURE 2019: Uncertainty quantification and noise modelling -- Probabilistic Surface Reconstruction with Unknown Correspondence -- Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty -- Propagating Uncertainty Across Cascaded Medical Imaging Tasks For Improved Deep Learning Inference -- Reg R-CNN: Lesion Detection and Grading under Noisy Labels -- Fast Nonparametric Mutual Information based Registration and Uncertainty Estimation -- Quantifying Uncertainty of deep neural networks in skin lesion classification -- UNSURE 2019: Domain shift robustness -- A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data -- Out of distribution detection for intra-operative functional imaging -- CLIP 2019 -- A Clinical Measuring Platform for Building the Bridge across the Quantification of Pathological N-cells in Medical Imaging for Studies of Disease -- Spatiotemporal statistical model of anatomical landmarks on a human embryonic brain -- Spaciousness filters for non-contrast CT volume segmentation of the intestine region for emergency ileus diagnosis -- Recovering physiological changes in nasal anatomy with confidence estimates -- Synthesis of Medical Images Using GANs -- DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation -- Multi-instance deep learning with graph convolutional neural networks for diagnosis of kidney diseases using ultrasound imaging -- Data Augmentation from Sketch -- An automated CNN-based 3D anatomical landmark detection method to facilitate surface-based 3D facial shape analysis -- A Device-independent Novel Statistical Modeling for Cerebral TOF-MRA data Segmentation -- Three-dimensional face reconstruction from uncalibrated photographs: application to early detection of genetic syndromes.
Contained By:
Springer eBooks
標題:
Diagnostic imaging - Congresses. - Data processing -
電子資源:
https://doi.org/10.1007/978-3-030-32689-0
ISBN:
9783030326890
Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures = first International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures
first International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /[electronic resource] :UNSURE 2019edited by Hayit Greenspan ... [et al.]. - Cham :Springer International Publishing :2019. - xvii, 192 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,118400302-9743 ;. - Lecture notes in computer science ;11840..
UNSURE 2019: Uncertainty quantification and noise modelling -- Probabilistic Surface Reconstruction with Unknown Correspondence -- Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty -- Propagating Uncertainty Across Cascaded Medical Imaging Tasks For Improved Deep Learning Inference -- Reg R-CNN: Lesion Detection and Grading under Noisy Labels -- Fast Nonparametric Mutual Information based Registration and Uncertainty Estimation -- Quantifying Uncertainty of deep neural networks in skin lesion classification -- UNSURE 2019: Domain shift robustness -- A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data -- Out of distribution detection for intra-operative functional imaging -- CLIP 2019 -- A Clinical Measuring Platform for Building the Bridge across the Quantification of Pathological N-cells in Medical Imaging for Studies of Disease -- Spatiotemporal statistical model of anatomical landmarks on a human embryonic brain -- Spaciousness filters for non-contrast CT volume segmentation of the intestine region for emergency ileus diagnosis -- Recovering physiological changes in nasal anatomy with confidence estimates -- Synthesis of Medical Images Using GANs -- DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation -- Multi-instance deep learning with graph convolutional neural networks for diagnosis of kidney diseases using ultrasound imaging -- Data Augmentation from Sketch -- An automated CNN-based 3D anatomical landmark detection method to facilitate surface-based 3D facial shape analysis -- A Device-independent Novel Statistical Modeling for Cerebral TOF-MRA data Segmentation -- Three-dimensional face reconstruction from uncalibrated photographs: application to early detection of genetic syndromes.
This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.
ISBN: 9783030326890
Standard No.: 10.1007/978-3-030-32689-0doiSubjects--Topical Terms:
893542
Diagnostic imaging
--Data processing--Congresses.
LC Class. No.: RC78.7.D53 / U57 2019
Dewey Class. No.: 616.0754
Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures = first International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
LDR
:04184nmm a2200385 a 4500
001
2218851
003
DE-He213
005
20191026142147.0
006
m d
007
cr nn 008maaau
008
201126s2019 sz s 0 eng d
020
$a
9783030326890
$q
(electronic bk.)
020
$a
9783030326883
$q
(paper)
024
7
$a
10.1007/978-3-030-32689-0
$2
doi
035
$a
978-3-030-32689-0
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
RC78.7.D53
$b
U57 2019
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
616.0754
$2
23
090
$a
RC78.7.D53
$b
U59 2019
111
2
$a
UNSURE (Workshop)
$n
(1st :
$d
2019 :
$c
Shenzhen Shi, China)
$3
3453626
245
1 0
$a
Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures
$h
[electronic resource] :
$b
first International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019 : proceedings /
$c
edited by Hayit Greenspan ... [et al.].
246
3
$a
UNSURE 2019
246
3
$a
CLIP 2019
246
3
$a
MICCAI 2019
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2019.
300
$a
xvii, 192 p. :
$b
ill. (some col.), digital ;
$c
24 cm.
490
1
$a
Lecture notes in computer science,
$x
0302-9743 ;
$v
11840
490
1
$a
Image processing, computer vision, pattern recognition, and graphics
505
0
$a
UNSURE 2019: Uncertainty quantification and noise modelling -- Probabilistic Surface Reconstruction with Unknown Correspondence -- Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty -- Propagating Uncertainty Across Cascaded Medical Imaging Tasks For Improved Deep Learning Inference -- Reg R-CNN: Lesion Detection and Grading under Noisy Labels -- Fast Nonparametric Mutual Information based Registration and Uncertainty Estimation -- Quantifying Uncertainty of deep neural networks in skin lesion classification -- UNSURE 2019: Domain shift robustness -- A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data -- Out of distribution detection for intra-operative functional imaging -- CLIP 2019 -- A Clinical Measuring Platform for Building the Bridge across the Quantification of Pathological N-cells in Medical Imaging for Studies of Disease -- Spatiotemporal statistical model of anatomical landmarks on a human embryonic brain -- Spaciousness filters for non-contrast CT volume segmentation of the intestine region for emergency ileus diagnosis -- Recovering physiological changes in nasal anatomy with confidence estimates -- Synthesis of Medical Images Using GANs -- DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation -- Multi-instance deep learning with graph convolutional neural networks for diagnosis of kidney diseases using ultrasound imaging -- Data Augmentation from Sketch -- An automated CNN-based 3D anatomical landmark detection method to facilitate surface-based 3D facial shape analysis -- A Device-independent Novel Statistical Modeling for Cerebral TOF-MRA data Segmentation -- Three-dimensional face reconstruction from uncalibrated photographs: application to early detection of genetic syndromes.
520
$a
This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.
650
0
$a
Diagnostic imaging
$x
Data processing
$v
Congresses.
$3
893542
650
0
$a
Artificial intelligence
$x
Medical applications
$x
Congresses.
$3
660945
650
0
$a
Machine learning
$x
Congresses.
$3
576368
650
1 4
$a
Artificial Intelligence.
$3
769149
650
2 4
$a
Image Processing and Computer Vision.
$3
891070
650
2 4
$a
Health Informatics.
$3
892928
700
1
$a
Greenspan, Hayit.
$3
1568348
710
2
$a
SpringerLink (Online service)
$3
836513
711
2
$a
CLIP (Workshop)|n(8th :
$d
2019 :
$c
Shenzhen Shi, China)
$3
3453628
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
11840.
$3
3453627
830
0
$a
Image processing, computer vision, pattern recognition, and graphics.
$3
3382509
856
4 0
$u
https://doi.org/10.1007/978-3-030-32689-0
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
W9393710
電子資源
11.線上閱覽_V
電子書
EB RC78.7.D53 U57 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