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Authors: Kush Gupta ; Amir Aly and Emmanuel Ifecahor

Affiliation: University of Plymouth, Plymouth, U.K.

Keyword(s): Cross-Domain Transfer Learning, Autism Diagnosis, Vision Transformers.

Abstract: A cross-domain transfer learning approach is introduced to address the challenges of diagnosing individuals with Autism Spectrum Disorder (ASD) using small-scale fMRI datasets. Vision Transformer (ViT) and TinyViT models pre-trained on the ImageNet, were employed to transfer knowledge from the natural image domain to the brain imaging domain. The models were fine-tuned on ABIDE and CMI-HBN, using a teacher-student framework with knowledge distillation loss. Experimental results demonstrated that our method out-performed previous studies, ViT models, and CNN-based models. Our approach achieved competitive performance (F-1 score 78.72%) with a much smaller parameter size. This study highlights the effectiveness of cross-domain transfer learning in medical applications, particularly for scenarios with small datasets. It suggests that pre-trained models can be leveraged to improve diagnostic accuracy for neuro-developmental disorders such as ASD. The findings indicate that the features l earned from natural images can be adapted to fMRI data using the proposed method, potentially providing a reliable and efficient approach to diagnosing autism. (More)

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Paper citation in several formats:
Gupta, K., Aly, A. and Ifecahor, E. (2025). Cross-Domain Transfer Learning for Domain Adaptation in Autism Spectrum Disorder Diagnosis. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF; ISBN 978-989-758-731-3; ISSN 2184-4305, SciTePress, pages 53-64. DOI: 10.5220/0013113000003911

@conference{healthinf25,
author={Kush Gupta and Amir Aly and Emmanuel Ifecahor},
title={Cross-Domain Transfer Learning for Domain Adaptation in Autism Spectrum Disorder Diagnosis},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF},
year={2025},
pages={53-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013113000003911},
isbn={978-989-758-731-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF
TI - Cross-Domain Transfer Learning for Domain Adaptation in Autism Spectrum Disorder Diagnosis
SN - 978-989-758-731-3
IS - 2184-4305
AU - Gupta, K.
AU - Aly, A.
AU - Ifecahor, E.
PY - 2025
SP - 53
EP - 64
DO - 10.5220/0013113000003911
PB - SciTePress

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