Influence-driven Sample Selection for Functional Brain Network Classification: Application to Autism Diagnosis

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Abstract
The proliferation of non-invasive neuroimaging datasets acquired from different modalities has driven advancements in machine learning models for diagnosing brain disorders. While prior studies have primarily focused on feature engineering and model architecture improvements, they often neglect the impact of low-quality samples in training datasets, which can significantly hinder diagnostic performance. To address this, we introduce a novel sample selection framework, Influence-based Detection of Opponent Samples (IDOS), which estimates sample quality using influences approximated by the change in loss relative to a reference point. We utilized Graph Convolutional Networks (GCN) and Differentiable Graph Pooling Modules (DIFFPOOL) in IDOS using an architecture that leverages whole-brain graphs. Excluding low-quality samples identified by IDOS significantly enhanced both models’ performance compared to the baseline, yielding average improvements of 6.89% and 7.15% across accuracy, precision, recall, and specificity, for GCN and DIFFPOOL, respectively. The proposed framework offers a generalizable solution for mitigating the impact of suboptimal samples.

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