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|a Medical Image Understanding and Analysis (Conference)
|n (24th :
|d 2020 :
|c Online)
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1 |
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|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.).
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|a MIUA 2020
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|a Cham :
|b Springer,
|c 2020.
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|a 1 online resource
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|a text
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|a online resource
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|a Communications in computer and information science,
|x 1865-0929 ;
|v 1248
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|a International conference proceedings.
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|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
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|a Includes author index.
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|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 |
|
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|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 |
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|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
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505 |
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|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 |
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|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
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505 |
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|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
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|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
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650 |
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|a Diagnostic imaging
|v Congresses.
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|a Diagnostic imaging
|x Data processing
|v Congresses.
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|a Machine learning.
|2 bicssc
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|a Information technology: general issues.
|2 bicssc
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|a Pattern recognition.
|2 bicssc
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|a Computer networking & communications.
|2 bicssc
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|a Educational equipment & technology, computer-aided learning (Calif.)
|2 bicssc
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650 |
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7 |
|a Image processing.
|2 bicssc
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650 |
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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
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650 |
|
7 |
|a Computers
|x Online Services
|x General.
|2 bisacsh
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650 |
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7 |
|a Education
|x Computers & Technology.
|2 bisacsh
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7 |
|a Computers
|x Computer Graphics.
|2 bisacsh
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7 |
|a Diagnostic imaging
|2 fast
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7 |
|a Diagnostic imaging
|x Data processing
|2 fast
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655 |
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|a Electronic books.
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655 |
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|a proceedings (reports)
|2 aat
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|a Conference papers and proceedings
|2 fast
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|a Conference papers and proceedings.
|2 lcgft
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|a Actes de congrès.
|2 rvmgf
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1 |
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|a Papież, Bartłomiej W.
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|a Namburete, Ana I. L.
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|a Yaqub, Mohammad.
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1 |
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|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 |
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|a SPRING-COMP2020
|
994 |
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|a 92
|b HCD
|