|
|
|
|
| LEADER |
00000cam a2200000 a 4500 |
| 001 |
on1180968847 |
| 003 |
OCoLC |
| 005 |
20240808213014.0 |
| 006 |
m o d |
| 007 |
cr |n||||||||| |
| 008 |
200803s2020 sz o 101 0 eng d |
| 040 |
|
|
|a YDX
|b eng
|e pn
|c YDX
|d GW5XE
|d DKU
|d EBLCP
|d LQU
|d SFB
|d UKMGB
|d OCLCF
|d NLW
|d UKAHL
|d COM
|d OCLCO
|d OCLCQ
|d BRX
|d OCLCQ
|d OCLCO
|d OCLCL
|d OCLCQ
|
| 015 |
|
|
|a GBC0E5657
|2 bnb
|
| 016 |
7 |
|
|a 019838009
|2 Uk
|
| 019 |
|
|
|a 1179000343
|a 1181836543
|a 1182446046
|a 1182913296
|a 1183933919
|a 1190684173
|a 1193273644
|a 1195447452
|a 1196137824
|a 1196166178
|a 1197542121
|a 1197551428
|a 1198138969
|a 1198818624
|
| 020 |
|
|
|a 9783030527914
|q (electronic bk.)
|
| 020 |
|
|
|a 3030527913
|q (electronic bk.)
|
| 020 |
|
|
|z 3030527905
|
| 020 |
|
|
|z 9783030527907
|
| 024 |
7 |
|
|a 10.1007/978-3-030-52791-4
|2 doi
|
| 024 |
8 |
|
|a 10.1007/978-3-030-52
|
| 035 |
|
|
|a (OCoLC)1180968847
|z (OCoLC)1179000343
|z (OCoLC)1181836543
|z (OCoLC)1182446046
|z (OCoLC)1182913296
|z (OCoLC)1183933919
|z (OCoLC)1190684173
|z (OCoLC)1193273644
|z (OCoLC)1195447452
|z (OCoLC)1196137824
|z (OCoLC)1196166178
|z (OCoLC)1197542121
|z (OCoLC)1197551428
|z (OCoLC)1198138969
|z (OCoLC)1198818624
|
| 037 |
|
|
|a com.springer.onix.9783030527914
|b Springer Nature
|
| 050 |
|
4 |
|a RC78.7.D53
|
| 072 |
|
7 |
|a UYT
|2 bicssc
|
| 072 |
|
7 |
|a COM012000
|2 bisacsh
|
| 072 |
|
7 |
|a UYT
|2 thema
|
| 072 |
|
7 |
|a UYQV
|2 thema
|
| 049 |
|
|
|a HCDD
|
| 111 |
2 |
|
|a Medical Image Understanding and Analysis (Conference)
|n (24th :
|d 2020 :
|c Online)
|
| 245 |
1 |
0 |
|a Medical image understanding and analysis :
|b 24th Annual Conference, MIUA 2020, Oxford, UK, July 15-17, 2020, Proceedings /
|c Bartłomiej W. Papież, Ana I. L. Namburete, Mohammad Yaqub, J. Alison Noble (eds.).
|
| 246 |
3 |
|
|a MIUA 2020
|
| 260 |
|
|
|a Cham :
|b Springer,
|c 2020.
|
| 300 |
|
|
|a 1 online resource
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 490 |
1 |
|
|a Communications in computer and information science,
|x 1865-0929 ;
|v 1248
|
| 500 |
|
|
|a International conference proceedings.
|
| 500 |
|
|
|a "The conference was supposed to be held at the St Anne's College, July 15-17 2020, in Oxford, UK, however due the COVID-19 pandemic the conference was, for the first time ever, organized as a fully virtual event."--Preface
|
| 500 |
|
|
|a Includes author index.
|
| 520 |
|
|
|a This book constitutes the refereed proceedings of the 24th Conference on Medical Image Understanding and Analysis, MIUA 2020, held in July 2020. Due to COVID-19 pandemic the conference was held virtually. The 29 full papers and 5 short papers presented were carefully reviewed and selected from 70 submissions. They were organized according to following topical sections: image segmentation; image registration, reconstruction and enhancement; radiomics, predictive models, and quantitative imaging biomarkers; ocular imaging analysis; biomedical simulation and modelling. --
|c Provided by publisher.
|
| 520 |
|
|
|a This book constitutes the refereed proceedings of the 24th Conference on Medical Image Understanding and Analysis, MIUA 2020, held in July 2020. Due to COVID-19 pandemic the conference was held virtually. The 29 full papers and 5 short papers presented were carefully reviewed and selected from 70 submissions. They were organized according to following topical sections: image segmentation; image registration, reconstruction and enhancement; radiomics, predictive models, and quantitative imaging biomarkers; ocular imaging analysis; biomedical simulation and modelling. --
|c Provided by publisher.
|
| 505 |
0 |
|
|a Intro -- Preface -- Organization -- Contents -- Image Segmentation -- Textural Feature Based Segmentation: A Repeatable and Accurate Segmentation Approach for Tumors in PET Images -- Abstract -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Segmentation Approaches -- 2.3 Evaluation of Segmentation Algorithm -- 3 Results -- 3.1 Comparison with Different Configurations of TF Algorithm -- 3.2 Comparison with Conventional Segmentation Approaches -- 4 Discussion and Conclusion -- Acknowledgements -- References
|
| 505 |
8 |
|
|a Unlearning Scanner Bias for MRI Harmonisation in Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Standard Supervised Training -- 2.2 Semi Supervised Learning -- 2.3 The Location of the Domain Predictor -- 3 Experimental Setup -- 4 Results -- 4.1 Supervised Unlearning -- 4.2 Semi Supervised Results -- 4.3 The Effect of the Location of the Domain Predictor -- 5 Discussion -- References -- Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema -- 1 Introduction -- 2 Related Work -- 3 Data -- 3.1 Segmentation Dataset
|
| 505 |
8 |
|
|a 3.2 Patch Extraction Technique -- 3.3 Binary Classification Dataset -- 3.4 Michigan-Columbia Dataset -- 4 Methods -- 4.1 U-Net -- 4.2 EU-Net -- 4.3 Binary Classification of MF vs. Eczema -- 5 Results -- 5.1 Segmentation on the Michigan-Columbia Dataset -- 5.2 Segmentation on the MF/E-Segmentation Dataset -- 5.3 Classification on the MF/E-Classification Dataset -- 6 Discussion -- 7 Future Work -- A Appendix -- A.1 Metrics -- A.2 EfficientNet Training Parameters -- A.3 U-Net Architecture -- A.4 EU-Net Architecture -- References
|
| 505 |
8 |
|
|a Autofocus Net: Auto-focused 3D CNN for Brain Tumour Segmentation -- 1 Introduction -- 2 Autofocus Net -- 2.1 WNet -- 2.2 Autofocus Layer -- 3 Implementation Details -- 3.1 Data -- 3.2 Training -- 3.3 Testing -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Cortical Plate Segmentation Using CNNs in 3D Fetal Ultrasound -- 1 Introduction -- 2 Network Design -- 3 Experimental Setup -- 3.1 Dataset -- 3.2 Atlas-Based Label Propagation -- 3.3 Network Implementation -- 4 Results -- 4.1 Cross Validation -- 4.2 Cortical Plate Segmentation -- 4.3 Sylvian Fissure -- 4.4 Atlas Averages
|
| 505 |
8 |
|
|a 5 Discussion and Conclusion -- References -- Improving U-Net Segmentation with Active Contour Based Label Correction -- 1 Introduction -- 2 Methods -- 2.1 Boundary Prediction -- 2.2 Active Contour Label Correction -- 2.3 Boundary Prediction Network -- 2.4 Datasets -- 2.5 Experiments -- 2.6 Implementation Details -- 3 Results -- 4 Conclusion -- References -- Segmenting Hepatocellular Carcinoma in Multi-phase CT -- 1 Introduction -- 2 Related Work -- 3 Methodology and Experimental Setup -- 3.1 Data and Pre-processing -- 3.2 Cascaded U-Nets -- 3.3 Single-Phase U-Nets -- 3.4 Early Fusion U-Nets
|
| 650 |
|
0 |
|a Diagnostic imaging
|v Congresses.
|
| 650 |
|
0 |
|a Diagnostic imaging
|x Data processing
|v Congresses.
|
| 650 |
|
7 |
|a Machine learning.
|2 bicssc
|
| 650 |
|
7 |
|a Information technology: general issues.
|2 bicssc
|
| 650 |
|
7 |
|a Pattern recognition.
|2 bicssc
|
| 650 |
|
7 |
|a Computer networking & communications.
|2 bicssc
|
| 650 |
|
7 |
|a Educational equipment & technology, computer-aided learning (Calif.)
|2 bicssc
|
| 650 |
|
7 |
|a Image processing.
|2 bicssc
|
| 650 |
|
7 |
|a Computers
|x Intelligence (AI) & Semantics.
|2 bisacsh
|
| 650 |
|
7 |
|a Computers
|x Data Processing.
|2 bisacsh
|
| 650 |
|
7 |
|a Computers
|x Computer Vision & Pattern Recognition.
|2 bisacsh
|
| 650 |
|
7 |
|a Computers
|x Online Services
|x General.
|2 bisacsh
|
| 650 |
|
7 |
|a Education
|x Computers & Technology.
|2 bisacsh
|
| 650 |
|
7 |
|a Computers
|x Computer Graphics.
|2 bisacsh
|
| 650 |
|
7 |
|a Diagnostic imaging
|2 fast
|
| 650 |
|
7 |
|a Diagnostic imaging
|x Data processing
|2 fast
|
| 655 |
|
0 |
|a Electronic books.
|
| 655 |
|
7 |
|a proceedings (reports)
|2 aat
|
| 655 |
|
7 |
|a Conference papers and proceedings
|2 fast
|
| 655 |
|
7 |
|a Conference papers and proceedings.
|2 lcgft
|
| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
|
| 700 |
1 |
|
|a Papież, Bartłomiej W.
|
| 700 |
1 |
|
|a Namburete, Ana I. L.
|
| 700 |
1 |
|
|a Yaqub, Mohammad.
|
| 700 |
1 |
|
|a Noble, J. Alison.
|
| 776 |
0 |
8 |
|i Print version:
|a Medical Image Understanding and Analysis (Conference) (24th : 2020 : Online).
|t Medical image understanding and analysis.
|d Cham : Springer, 2020
|z 3030527905
|z 9783030527907
|w (OCoLC)1164489189
|
| 830 |
|
0 |
|a Communications in computer and information science ;
|v 1248.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-52791-4
|y Click for online access
|
| 903 |
|
|
|a SPRING-COMP2020
|
| 994 |
|
|
|a 92
|b HCD
|