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

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Machine Learning in Clinical Neuroimaging (MLCN 2023)

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

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Abstract

Autism spectrum disorder (ASD) is a prevalent psychiatric condition characterized by atypical cognitive, emotional, and social patterns. Timely and accurate diagnosis is crucial for effective interventions and improved outcomes in individuals with ASD. In this study, we propose a novel Multi-Atlas Enhanced Transformer framework, METAFormer, ASD classification. Our framework utilizes resting-state functional magnetic resonance imaging data from the ABIDE I dataset, comprising 406 ASD and 476 typical control (TC) subjects. METAFormer employs a multi-atlas approach, where flattened connectivity matrices from the AAL, CC200, and DOS160 atlases serve as input to the transformer encoder. Notably, we demonstrate that self-supervised pretraining, involving the reconstruction of masked values from the input, significantly enhances classification performance without the need for additional or separate training data. Through stratified cross-validation, we evaluate the proposed framework and show that it surpasses state-of-the-art performance on the ABIDE I dataset, with an average accuracy of 83.7% and an AUC-score of 0.832. The code for our framework is available at github.com/Lugges991/METAFormer.

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Acknowledgements

The authors thank the International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction (IMPRS-MMFD) for supporting Samuel Heczko. Florian Birk is supported by the Deutsche Forschungsgesellschaft (DFG) Grant DFG HE 9297/1-1. Julius Steiglechner is funded by Alzheimer Forschung Initiative e.V.Grant #18052.

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Authors and Affiliations

  1. Max-Planck-Institute for Biological Cybernetics, 72076, Tübingen, Germany

    Lucas Mahler, Qi Wang, Julius Steiglechner, Florian Birk, Samuel Heczko, Klaus Scheffler & Gabriele Lohmann

  2. University Hospital Tübingen, 72076, Tübingen, Germany

    Julius Steiglechner, Florian Birk, Klaus Scheffler & Gabriele Lohmann

Authors
  1. Lucas Mahler
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  2. Qi Wang
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  3. Julius Steiglechner
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  4. Florian Birk
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  5. Samuel Heczko
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  6. Klaus Scheffler
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  7. Gabriele Lohmann
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Corresponding author

Correspondence to Lucas Mahler .

Editor information

Editors and Affiliations

  1. Zurich University of Applied Sciences, Winterthur, Switzerland

    Ahmed Abdulkadir

  2. Indian Institute of Technology Ropar, Rupnagar, India

    Deepti R. Bathula

  3. Yale University, New Haven, CT, USA

    Nicha C. Dvornek

  4. University of Pennsylvania, Philadelphia, PA, USA

    Sindhuja T. Govindarajan

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

    Mohamad Habes

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

    Vinod Kumar

  7. University of Oslo, Oslo, Norway

    Esten Leonardsen

  8. University of Tübingen, Tübingen, Germany

    Thomas Wolfers

  9. Concordia University, Montréal, QC, Canada

    Yiming Xiao

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Mahler, L. et al. (2023). Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2023. Lecture Notes in Computer Science, vol 14312. Springer, Cham. https://doi.org/10.1007/978-3-031-44858-4_12

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  • DOI: https://doi.org/10.1007/978-3-031-44858-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44857-7

  • Online ISBN: 978-3-031-44858-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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