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arXiv · 2504.09463

Comorbidity-Informed Transfer Learning for Neuro-developmental Disorder Diagnosis

Xin Wen·Shijie Guo·Wenbo Ning·Rui Cao·Jie Xiang·Xiaobo Liu·Jintai Chen
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Abstract

Neuro-developmental disorders are manifested as dysfunctions in cognition, communication, behaviour and adaptability, and deep learning-based computer-aided diagnosis (CAD) can alleviate the increasingly strained healthcare resources on neuroimaging. However, neuroimaging such as fMRI contains complex spatio-temporal features, which makes the corresponding representations susceptible to a variety of distractions, thus leading to less effective in CAD. For the first time, we present a Comorbidity-Informed Transfer Learning(CITL) framework for diagnosing neuro-developmental disorders using fMRI. In CITL, a new reinforced representation generation network is proposed, which first combines transfer learning with pseudo-labelling to remove interfering patterns from the temporal domain of fMRI and generates new representations using encoder-decoder architecture. The new representations are then trained in an architecturally simple classification network to obtain CAD model. In particular, the framework fully considers the comorbidity mechanisms of neuro-developmental disorders and effectively integrates them with semi-supervised learning and transfer learning, providing new perspectives on interdisciplinary. Experimental results demonstrate that CITL achieves competitive accuracies of 76.32% and 73.15% for detecting autism spectrum disorder and attention deficit hyperactivity disorder, respectively, which outperforms existing related transfer learning work for 7.2% and 0.5% respectively.

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BibTeXRIS

Xin Wen, Shijie Guo, Wenbo Ning, Rui Cao, Jie Xiang, Xiaobo Liu, Jintai Chen. 2025-04-13. Comorbidity-Informed Transfer Learning for Neuro-developmental Disorder Diagnosis. https://doi.org/10.1016/j.neunet.2026.108980

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