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Machine Learning in Clinical Neuroimaging

5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Ahmed Abdulkadir

    1. Lausanne University Hospital, Lausanne, Switzerland

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  2. Deepti R. Bathula

    1. Indian Institute of Technology Ropar, Rupnagar, India

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  3. Nicha C. Dvornek

    1. Yale University, New Haven, USA

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  4. Mohamad Habes

    1. The University of Texas Health Science Center, San Antonio, USA

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  5. Seyed Mostafa Kia

    1. Donders Institute, Nijmegen, The Netherlands

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  6. Vinod Kumar

    1. Max Planck Institute for Biological Cybernetics, Tübingen, Germany

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  7. Thomas Wolfers

    1. University of Tübingen, Tübingen, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13596)

Included in the following conference series:

Conference proceedings info: MLCN 2022.

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Table of contents (17 papers)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Morphometry

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Joint Reconstruction and Parcellation of Cortical Surfaces

      • Anne-Marie Rickmann, Fabian Bongratz, Sebastian Pölsterl, Ignacio Sarasua, Christian Wachinger
      Pages 3-12

    3. A Study of Demographic Bias in CNN-Based Brain MR Segmentation

      • Stefanos Ioannou, Hana Chockler, Alexander Hammers, Andrew P. King, for the Alzheimer’s Disease Neuroimaging Initiative
      Pages 13-22

    4. Volume is All You Need: Improving Multi-task Multiple Instance Learning for WMH Segmentation and Severity Estimation

      • Wooseok Jung, Chong Hyun Suh, Woo Hyun Shim, Jinyoung Kim, Dongsoo Lee, Changhyun Park et al.
      Pages 23-31

    5. Self-supervised Test-Time Adaptation for Medical Image Segmentation

      • Hao Li, Han Liu, Dewei Hu, Jiacheng Wang, Hans Johnson, Omar Sherbini et al.
      Pages 32-41

    6. Accurate Hippocampus Segmentation Based on Self-supervised Learning with Fewer Labeled Data

      • Kassymzhomart Kunanbayev, Donggon Jang, Woojin Jeong, Nahyun Kim, Dae-Shik Kim
      Pages 42-51

    7. Concurrent Ischemic Lesion Age Estimation and Segmentation of CT Brain Using a Transformer-Based Network

      • Adam Marcus, Paul Bentley, Daniel Rueckert
      Pages 52-62

    8. Weakly Supervised Intracranial Hemorrhage Segmentation Using Hierarchical Combination of Attention Maps from a Swin Transformer

      • Amirhossein Rasoulian, Soorena Salari, Yiming Xiao
      Pages 63-72

    9. Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma

      • Navodini Wijethilake, Aaron Kujawa, Reuben Dorent, Muhammad Asad, Anna Oviedova, Tom Vercauteren et al.
      Pages 73-82

    10. Neuroimaging Harmonization Using cGANs: Image Similarity Metrics Poorly Predict Cross-Protocol Volumetric Consistency

      • Veronica Ravano, Jean-François Démonet, Daniel Damian, Reto Meuli, Gian Franco Piredda, Till Huelnhagen et al.
      Pages 83-92

  3. Diagnostics, Aging, and Neurodegeneration

    1. Front Matter

      Pages 93-93
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    2. Non-parametric ODE-Based Disease Progression Model of Brain Biomarkers in Alzheimer’s Disease

      • Matías Bossa, Abel Díaz Berenguer, Hichem Sahli
      Pages 95-103

    3. Lifestyle Factors That Promote Brain Structural Resilience in Individuals with Genetic Risk Factors for Dementia

      • Elizabeth Haddad, Shayan Javid, Nikhil Dhinagar, Alyssa H. Zhu, Pradeep Lam, Iyad Ba Gari et al.
      Pages 104-114

    4. Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging

      • Yuji Zhao, Max A. Laansma, Eva M. van Heese, Conor Owens-Walton, Laura M. Parkes, Ines Debove et al.
      Pages 115-124

    5. Augmenting Magnetic Resonance Imaging with Tabular Features for Enhanced and Interpretable Medial Temporal Lobe Atrophy Prediction

      • Dongsoo Lee, Chong Hyun Suh, Jinyoung Kim, Wooseok Jung, Changhyun Park, Kyu-Hwan Jung et al.
      Pages 125-134

    6. Automatic Lesion Analysis for Increased Efficiency in Outcome Prediction of Traumatic Brain Injury

      • Margherita Rosnati, Eyal Soreq, Miguel Monteiro, Lucia Li, Neil S. N. Graham, Karl Zimmerman et al.
      Pages 135-146

    7. Autism Spectrum Disorder Classification Based on Interpersonal Neural Synchrony: Can Classification be Improved by Dyadic Neural Biomarkers Using Unsupervised Graph Representation Learning?

      • Christian Gerloff, Kerstin Konrad, Jana Kruppa, Martin Schulte-Rüther, Vanessa Reindl
      Pages 147-157

    8. fMRI-S4: Learning Short- and Long-Range Dynamic fMRI Dependencies Using 1D Convolutions and State Space Models

      • Ahmed El-Gazzar, Rajat Mani Thomas, Guido van Wingen
      Pages 158-168

    9. Data Augmentation via Partial Nonlinear Registration for Brain-Age Prediction

      • Marc-Andre Schulz, Alexander Koch, Vanessa Emanuela Guarino, Dagmar Kainmueller, Kerstin Ritter
      Pages 169-178
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  1. Machine Learning in Clinical Neuroimaging

Keywords

About this book

This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with MICCAI 2022, Singapore in September 2022. 

The book includes 17 papers which were carefully reviewed and selected from 23 full-length submissions.
The 5th international workshop on Machine Learning in Clinical Neuroimaging (MLCN2022) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track).




The papers are categorzied into topical sub-headings: Morphometry; Diagnostics, and Aging, and Neurodegeneration. 

Editors and Affiliations

  • Lausanne University Hospital, Lausanne, Switzerland

    Ahmed Abdulkadir

  • Indian Institute of Technology Ropar, Rupnagar, India

    Deepti R. Bathula

  • Yale University, New Haven, USA

    Nicha C. Dvornek

  • The University of Texas Health Science Center, San Antonio, USA

    Mohamad Habes

  • Donders Institute, Nijmegen, The Netherlands

    Seyed Mostafa Kia

  • Max Planck Institute for Biological Cybernetics, Tübingen, Germany

    Vinod Kumar

  • University of Tübingen, Tübingen, Germany

    Thomas Wolfers

Bibliographic Information

  • Book Title: Machine Learning in Clinical Neuroimaging

  • Book Subtitle: 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings

  • Editors: Ahmed Abdulkadir, Deepti R. Bathula, Nicha C. Dvornek, Mohamad Habes, Seyed Mostafa Kia, Vinod Kumar, Thomas Wolfers

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-17899-3

  • 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 2022

  • Softcover ISBN: 978-3-031-17898-6Published: 08 October 2022

  • eBook ISBN: 978-3-031-17899-3Published: 07 October 2022

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 180

  • Number of Illustrations: 7 b/w illustrations, 49 illustrations in colour

  • Topics: Image Processing and Computer Vision, Machine Learning, Computing Milieux, Computer Appl. in Social and Behavioral Sciences

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