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Machine Learning in Clinical Neuroimaging

6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Ahmed Abdulkadir

    1. Zurich University of Applied Sciences, Winterthur, 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. Sindhuja T. Govindarajan

    1. University of Pennsylvania, Philadelphia, USA

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  5. Mohamad Habes

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

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  6. Vinod Kumar

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

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  7. Esten Leonardsen

    1. University of Oslo, Oslo, Norway

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  8. Thomas Wolfers

    1. University of Tübingen, Tübingen, Germany

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  9. Yiming Xiao

    1. Concordia University, Montréal, Canada

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14312)

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Conference proceedings info: MLCN 2023.

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About this book

This book constitutes the refereed proceedings of the 6th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2023, held in Conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. 
The book includes 16 papers which were carefully reviewed and selected from 28 full-length submissions.
The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) 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 on Machine Learning and Clinical Applications.

Keywords

Table of contents (16 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Machine Learning

    1. Front Matter

      Pages 1-1
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    2. Image-to-Image Translation Between Tau Pathology and Neuronal Metabolism PET in Alzheimer Disease with Multi-domain Contrastive Learning

      • Michael Tran Duong, Sandhitsu R. Das, Pulkit Khandelwal, Xueying Lyu, Long Xie, Paul A. Yushkevich et al.
      Pages 3-13

    3. Multi-shell dMRI Estimation from Single-Shell Data via Deep Learning

      • Reagan Dugan, Owen Carmichael
      Pages 14-22

    4. A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging

      • Qi Wang, Lucas Mahler, Julius Steiglechner, Florian Birk, Klaus Scheffler, Gabriele Lohmann
      Pages 23-33

    5. Cross-Attention for Improved Motion Correction in Brain PET

      • Zhuotong Cai, Tianyi Zeng, Eléonore V. Lieffrig, Jiazhen Zhang, Fuyao Chen, Takuya Toyonaga et al.
      Pages 34-45

    6. VesselShot: Few-shot Learning for Cerebral Blood Vessel Segmentation

      • Mumu Aktar, Hassan Rivaz, Marta Kersten-Oertel, Yiming Xiao
      Pages 46-55

    7. WaveSep: A Flexible Wavelet-Based Approach for Source Separation in Susceptibility Imaging

      • Zhenghan Fang, Hyeong-Geol Shin, Peter van Zijl, Xu Li, Jeremias Sulam
      Pages 56-66

    8. Joint Estimation of Neural Events and Hemodynamic Response Functions from Task fMRI via Convolutional Neural Networks

      • Kai-Cheng Chuang, Sreekrishna Ramakrishnapillai, Krystal Kirby, Arend W. A. Van Gemmert, Lydia Bazzano, Owen T. Carmichael
      Pages 67-78

    9. Learning Sequential Information in Task-Based fMRI for Synthetic Data Augmentation

      • Jiyao Wang, Nicha C. Dvornek, Lawrence H. Staib, James S. Duncan
      Pages 79-88

  3. Clinical Applications

    1. Front Matter

      Pages 89-89
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    2. Causal Sensitivity Analysis for Hidden Confounding: Modeling the Sex-Specific Role of Diet on the Aging Brain

      • Elizabeth Haddad, Myrl G. Marmarelis, Talia M. Nir, Aram Galstyan, Greg Ver Steeg, Neda Jahanshad
      Pages 91-101

    3. MixUp Brain-Cortical Augmentations in Self-supervised Learning

      • Corentin Ambroise, Vincent Frouin, Benoit Dufumier, Edouard Duchesnay, Antoine Grigis
      Pages 102-111

    4. Brain Age Prediction Based on Head Computed Tomography Segmentation

      • Artur Paulo, Fabiano Filho, Tayran Olegário, Bruna Pinto, Rafael Loureiro, Guilherme Ribeiro et al.
      Pages 112-122

    5. Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification

      • Lucas Mahler, Qi Wang, Julius Steiglechner, Florian Birk, Samuel Heczko, Klaus Scheffler et al.
      Pages 123-132

    6. Copy Number Variation Informs fMRI-Based Prediction of Autism Spectrum Disorder

      • Nicha C. Dvornek, Catherine Sullivan, James S. Duncan, Abha R. Gupta
      Pages 133-142

    7. Deep Attention Assisted Multi-resolution Networks for the Segmentation of White Matter Hyperintensities in Postmortem MRI Scans

      • Anoop Benet Nirmala, Tanweer Rashid, Elyas Fadaee, Nicolas Honnorat, Karl Li, Sokratis Charisis et al.
      Pages 143-152

    8. Stroke Outcome and Evolution Prediction from CT Brain Using a Spatiotemporal Diffusion Autoencoder

      • Adam Marcus, Paul Bentley, Daniel Rueckert
      Pages 153-162

    9. Morphological Versus Functional Network Organization: A Comparison Between Structural Covariance Networks and Probabilistic Functional Modes

      • Petra Lenzini, Tom Earnest, Sung Min Ha, Abdalla Bani, Aristeidis Sotiras, Janine Bijsterbosch
      Pages 163-172

  4. Back Matter

    Pages 173-174
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Other volumes

  1. Machine Learning in Clinical Neuroimaging

Editors and Affiliations

  • Zurich University of Applied Sciences, Winterthur, Switzerland

    Ahmed Abdulkadir

  • Indian Institute of Technology Ropar, Rupnagar, India

    Deepti R. Bathula

  • Yale University, New Haven, USA

    Nicha C. Dvornek

  • University of Pennsylvania, Philadelphia, USA

    Sindhuja T. Govindarajan

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

    Mohamad Habes

  • Max Planck Institute for Biological Cybernetics, Tübingen, Germany

    Vinod Kumar

  • University of Oslo, Oslo, Norway

    Esten Leonardsen

  • University of Tübingen, Tübingen, Germany

    Thomas Wolfers

  • Concordia University, Montréal, Canada

    Yiming Xiao

Bibliographic Information

  • Book Title: Machine Learning in Clinical Neuroimaging

  • Book Subtitle: 6th International Workshop, MLCN 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

  • Editors: Ahmed Abdulkadir, Deepti R. Bathula, Nicha C. Dvornek, Sindhuja T. Govindarajan, Mohamad Habes, Vinod Kumar, Esten Leonardsen, Thomas Wolfers, … Yiming Xiao

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-44858-4

  • 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 2023

  • Softcover ISBN: 978-3-031-44857-7Published: 08 October 2023

  • eBook ISBN: 978-3-031-44858-4Published: 07 October 2023

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: X, 174

  • Number of Illustrations: 5 b/w illustrations, 47 illustrations in colour

  • Topics: Image Processing and Computer Vision, Machine Learning, Computing Milieux, Computer Applications

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