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Medical Image Understanding and Analysis

28th Annual Conference, MIUA 2024, Manchester, UK, July 24–26, 2024, Proceedings, Part I

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Moi Hoon Yap

    1. Manchester Metropolitan University, Manchester, UK

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  2. Connah Kendrick

    1. Manchester Metropolitan University, Manchester, UK

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  3. Ardhendu Behera

    1. Edge Hill University, Ormskirk, UK

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  4. Timothy Cootes

    1. Aberystwyth University, Aberystwyth, UK

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  5. Reyer Zwiggelaar

    1. Aberystwyth University, Aberystwyth, UK

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14859)

Included in the following conference series:

Conference proceedings info: MIUA 2024.

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About this book

This two-volume set LNCS 14859-14860 constitutes the proceedings of the 28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024, held in Manchester, UK, during July 24–26, 2024.

The 59 full papers included in this book were carefully reviewed and selected from 93 submissions. They were organized in topical sections as follows: 

Part I : Advancement in Brain Imaging; Medical Images and Computational Models; and Digital Pathology, Histology and Microscopic Imaging.

Part II : Dental and Bone Imaging; Enhancing Low-Quality Medical Images; Domain Adaptation and Generalisation; and Dermatology, Cardiac Imaging and Other Medical Imaging.

Keywords

Table of contents (29 papers)

  1. Front Matter

    Pages i-xx
    Download chapter PDF

  2. Advancement in Brain Imaging

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Robust Multi-modal Registration of Cerebral Vasculature

      • Bertram Sabrowsky-Hirsch, Ahmed Alshenoudy, Josef Scharinger, Matthias Gmeiner, Stefan Thumfart, Michael Giretzlehner
      Pages 3-18

    3. Towards Segmenting Cerebral Arteries from Structural MRI

      • Ahmed Alshenoudy, Bertram Sabrowsky-Hirsch, Josef Scharinger, Stefan Thumfart, Michael Giretzlehner
      Pages 19-33

    4. Stochastic Uncertainty Quantification Techniques Fail to Account for Inter-analyst Variability in White Matter Hyperintensity Segmentation

      • Ben Philps, Maria del C. Valdes Hernandez, Susana Munoz Maniega, Mark E. Bastin, Eleni Sakka, Una Clancy et al.
      Pages 34-53

    5. Learning-Based MRI Response Predictions from OCT Microvascular Models to Replace Simulation-Based Frameworks

      • Jaloliddin Rustamov, Zahiriddin Rustamov, Nadia Badawi, Frederic Lesage, Nazar Zaki, Rafat Damseh
      Pages 54-67

    6. Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field

      • Lan Jiang, Yuchao Zheng, Miao Yu, Haiqing Zhang, Fatemah Aladwani, Alessandro Perelli
      Pages 68-80

    7. DeepDSMRI: Deep Domain Shift Analyzer for MRI

      • Rafsanjany Kushol, Sanjay Kalra, Yee-Hong Yang
      Pages 81-95

    8. Self-Supervised Pretraining for Cortical Surface Analysis

      • Dániel Unyi, Bálint Gyires-Tóth
      Pages 96-108

    9. Spike Detection in Deep Brain Stimulation Surgery with Convolutional Neural Networks

      • Arkadiusz Nowacki, Ewelina Kołpa, Mateusz Szychiewicz, Konrad Ciecierski, Ewa Niewiadomska-Szynkiewicz
      Pages 109-121

  3. Medical Images and Computational Models

    1. Front Matter

      Pages 123-123
      Download chapter PDF

    2. Micro-CT Imaging Techniques for Visualising Pinniped Mystacial Pad Musculature

      • Elizabeth Evans, Alyx Elder
      Pages 125-141

    3. SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images

      • Krithika Iyer, Jadie Adams, Shireen Y. Elhabian
      Pages 142-157

    4. JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA

      • Zeyu Zhang, Xuyin Qi, Mingxi Chen, Guangxi Li, Ryan Pham, Ayub Qassim et al.
      Pages 158-172

    5. Identification of Skin Diseases Based on Blind Chromophore Separation and Artificial Intelligence

      • Mustapha Zokay, Hicham Saylani
      Pages 173-187

    6. Generating Chest Radiology Report Findings Using a Multimodal Method

      • Chenyu Wang, Vladimir Janjic, Stephen McKenna
      Pages 188-201

    7. Image Processing and Machine Learning Techniques for Chagas Disease Detection and Identification

      • Lavdie Rada, Inass Azzawi, Preet Kumar, Carlos Francisco Brito-Loeza, Cefa Karabağ, Constantino C. Reyes-Aldasoro
      Pages 202-216

    8. Ensemble Deep Learning Models for Segmentation of Prostate Zonal Anatomy and Pathologically Suspicious Areas

      • Jakub Mitura, Rafał Jóźwiak, Jan Mycka, Ihor Mykhalevych, Michał Gonet, Piotr Sobecki et al.
      Pages 217-231

    9. Image Reconstruction for Proton Therapy Range Verification via U-NETs

      • Lena M. Setterdahl, William R. B. Lionheart, Sean Holman, Kyrre Skjerdal, Hunter N. Ratliff, Kristian Smeland Ytre-Hauge et al.
      Pages 232-244

    10. DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images

      • Mohammad Areeb Qazi, Ibrahim Almakky, Anees Ur Rehman Hashmi, Santosh Sanjeev, Mohammad Yaqub
      Pages 245-257
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Other volumes

  1. Medical Image Understanding and Analysis

  2. Medical Image Understanding and Analysis

Editors and Affiliations

  • Manchester Metropolitan University, Manchester, UK

    Moi Hoon Yap, Connah Kendrick

  • Edge Hill University, Ormskirk, UK

    Ardhendu Behera

  • Aberystwyth University, Aberystwyth, UK

    Timothy Cootes, Reyer Zwiggelaar

Bibliographic Information

  • Book Title: Medical Image Understanding and Analysis

  • Book Subtitle: 28th Annual Conference, MIUA 2024, Manchester, UK, July 24–26, 2024, Proceedings, Part I

  • Editors: Moi Hoon Yap, Connah Kendrick, Ardhendu Behera, Timothy Cootes, Reyer Zwiggelaar

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-66955-2

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

  • Softcover ISBN: 978-3-031-66954-5Published: 24 July 2024

  • eBook ISBN: 978-3-031-66955-2Published: 23 July 2024

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XX, 420

  • Number of Illustrations: 15 b/w illustrations, 150 illustrations in colour

  • Topics: Image Processing and Computer Vision, Artificial Intelligence, Computing Milieux, Computer Applications

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