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  • © 2021

Machine Learning in Clinical Neuroimaging

4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

Conference proceedings info: MLCN 2021.

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Table of contents (17 papers)

  1. Front Matter

    Pages i-xi
  2. Computational Anatomy

    1. Front Matter

      Pages 1-1
    2. Unfolding the Medial Temporal Lobe Cortex to Characterize Neurodegeneration Due to Alzheimer’s Disease Pathology Using Ex vivo Imaging

      • Sadhana Ravikumar, Laura Wisse, Sydney Lim, David Irwin, Ranjit Ittyerah, Long Xie et al.
      Pages 3-12
    3. Distinguishing Healthy Ageing from Dementia: A Biomechanical Simulation of Brain Atrophy Using Deep Networks

      • Mariana Da Silva, Carole H. Sudre, Kara Garcia, Cher Bass, M. Jorge Cardoso, Emma C. Robinson
      Pages 13-22
    4. Towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows

      • Matthias Wilms, Pauline Mouches, Jordan J. Bannister, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert
      Pages 23-33
    5. Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients

      • Verónica Muñoz-Ramírez, Nicolas Pinon, Florence Forbes, Carole Lartizen, Michel Dojat
      Pages 34-43
    6. MRI Image Registration Considerably Improves CNN-Based Disease Classification

      • Malte Klingenberg, Didem Stark, Fabian Eitel, Kerstin Ritter, for the Alzheimer’s Disease Neuroimaging Initiative
      Pages 44-52
    7. Dynamic Sub-graph Learning for Patch-Based Cortical Folding Classification

      • Zhiwei Deng, Jiong Zhang, Yonggang Shi, the Health and Aging Brain Study (HABS-HD) Study Team
      Pages 53-62
    8. Detection of Abnormal Folding Patterns with Unsupervised Deep Generative Models

      • Louise Guillon, Bastien Cagna, Benoit Dufumier, Joël Chavas, Denis Rivière, Jean-François Mangin
      Pages 63-72
    9. PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction

      • Qiang Ma, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary
      Pages 73-81
    10. Multi-modal Brain Segmentation Using Hyper-Fused Convolutional Neural Network

      • Wenting Duan, Lei Zhang, Jordan Colman, Giosue Gulli, Xujiong Ye
      Pages 82-91
    11. Robust Hydrocephalus Brain Segmentation via Globally and Locally Spatial Guidance

      • Yuanfang Qiao, Haoyi Tao, Jiayu Huo, Wenjun Shen, Qian Wang, Lichi Zhang
      Pages 92-100
  3. Brain Networks and Time Series

    1. Front Matter

      Pages 101-101
    2. Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation

      • Logan Z. J. Williams, Abdulah Fawaz, Matthew F. Glasser, A. David Edwards, Emma C. Robinson
      Pages 103-112
    3. Deep Stacking Networks for Conditional Nonlinear Granger Causal Modeling of fMRI Data

      • Kai-Cheng Chuang, Sreekrishna Ramakrishnapillai, Lydia Bazzano, Owen T. Carmichael
      Pages 113-124
    4. Dynamic Adaptive Spatio-Temporal Graph Convolution for fMRI Modelling

      • Ahmed El-Gazzar, Rajat Mani Thomas, Guido van Wingen
      Pages 125-134
    5. Structure-Function Mapping via Graph Neural Networks

      • Yang Ji, Samuel Deslauriers-Gauthier, Rachid Deriche
      Pages 135-144
    6. Improving Phenotype Prediction Using Long-Range Spatio-Temporal Dynamics of Functional Connectivity

      • Simon Dahan, Logan Z. J. Williams, Daniel Rueckert, Emma C. Robinson
      Pages 145-154
    7. H3K27M Mutations Prediction for Brainstem Gliomas Based on Diffusion Radiomics Learning

      • Ne Yang, Xiong Xiao, Xianyu Wang, Guocan Gu, Liwei Zhang, Hongen Liao
      Pages 155-164
    8. Constrained Learning of Task-Related and Spatially-Coherent Dictionaries from Task fMRI Data

      • Sreekrishna Ramakrishnapillai, Harris R. Lieberman, Jennifer C. Rood, Stefan M. Pasiakos, Kori Murray, Preetham Shankapal et al.
      Pages 165-173

Other Volumes

  1. Machine Learning in Clinical Neuroimaging

    4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

About this book

This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021, held on September 27, 2021, in conjunction with MICCAI 2021. The workshop was held virtually due to the COVID-19 pandemic. 

The 17 papers presented in this book were carefully reviewed and selected from 27 submissions. They were organized in topical sections named: computational anatomy and brain networks and time series.


Keywords

  • artificial intelligence
  • bioinformatics
  • brain mapping
  • clinical neuroimaging
  • computational anatomy
  • computer vision
  • deep learning
  • functional magnetic resonance imaging
  • image enhancement
  • image processing
  • image reconstruction
  • machine learning
  • medical image analysis
  • medical image computing
  • medical images
  • neural networks
  • pattern recognition

Editors and Affiliations

  • University of Pennsylvania, Philadelphia, USA

    Ahmed Abdulkadir

  • Donders Institute, Nijmegen, The Netherlands

    Seyed Mostafa Kia

  • The 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 College London, London, UK

    Jane Maryam Rondina

  • University Medical Center Utrecht, Utrecht, The Netherlands

    Chantal Tax

  • University of Oslo, Oslo, Norway

    Thomas Wolfers

Bibliographic Information

Buying options

eBook USD 44.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-87586-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 59.99
Price excludes VAT (USA)