Comorbidity-Informed Transfer Learning for Neuro-developmental Disorder Diagnosis

• URL: http://arxiv.org/abs/2504.09463v1
• Date: Sun, 13 Apr 2025 07:30:55 GMT
• Title: Comorbidity-Informed Transfer Learning for Neuro-developmental Disorder Diagnosis
• Authors: Xin Wen, Shijie Guo, Wenbo Ning, Rui Cao, Jie Xiang, Xiaobo Liu, Jintai Chen,
• Abstract summary: Comorbidity-In Transfer Learning framework for neuro-developmental disorders using fMRI.<n>New reinforced representation generation network is proposed.<n>Results demonstrate that CITL achieves competitive accuracies of 76.32% and 73.15% for detecting autism spectrum disorder and attention deficit hyperactivity disorder.
• Score: 26.634912866633925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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