Medical image understanding and analysis : 24th Annual Conference, MIUA 2020, Oxford, UK, July 15-17, 2020, Proceedings /: 24th Annual Conference, MIUA 2020, Oxford, UK, July 15-17, 2020, Proceedings. (2020)

• Record Type: Book
• Other Titles: MIUA 2020
• Further Information: Note: Bartłomiej W. Papież, Ana I. L. Namburete, Mohammad Yaqub, J. Alison Noble (eds.).
• Other Names: Papież, Bartłomiej W; Namburete, Ana I. L; Yaqub, Mohammad; Noble, J. Alison; Medical Image Understanding and Analysis (Conference), 24th
• Contents: 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 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 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 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 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
• Publisher Details: Cham : Springer
• Publication Date: 2020
• Extent: 1 online resource
• Subjects: 616.07/54; Diagnostic imaging -- Congresses; Diagnostic imaging -- Data processing -- Congresses; Electronic books; Electronic books
• Languages: English
• ISBNs: 9783030527914; 3030527913
• Related ISBNs: 3030527905; 9783030527907
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