利用多图脑数据量化图神经网络的复制能力
Quantifying the Reproducibility of Graph Neural Networks using Multigraph Brain Data
摘要：
图神经网络（GNN）在解决计算机视觉、计算机辅助诊断和相关领域中的若干问题方面已经见证了前所未有的增长。虽然之前的研究都集中在提高模型的准确性上，但量化GNN识别的最具鉴别力的特征的可重复性仍然是一个完整的问题，特别是在临床应用中，人们对GNN的可靠性表示担忧。具体而言，生物标志物在临床数据集之间的再现性和在类别之间的分布偏移（例如，健康和紊乱的大脑）对于揭示疾病的基本机制以及推动个性化治疗的发展至关重要。出于这些问题的动机，我们首次提出了基于再现性的GNN选择（RG-Select），这是一个通过量化最具鉴别力的特征（即，生物标志物）在不同模型之间共享。为了确定我们的框架的合理性，再现性评估包括不同因素的变化，如训练策略和数据扰动。尽管存在这些挑战，我们的框架成功地在不同的训练策略和各种临床数据集上得出了可复制的结论。因此，我们的研究结果可以为计算机辅助诊断和预后任务的生物标志物可信度和可靠性评估方法的发展铺平道路。RG-Select代码可在GitHub上获得，网址为https://github.com/basiralab/RG-Select。
英文摘要：
Graph neural networks (GNNs) have witnessed an unprecedented proliferation in tackling several problems in computer vision, computer-aided diagnosis, and related fields. While prior studies have focused on boosting the model accuracy, quantifying the reproducibility of the most discriminative features identified by GNNs is still an intact problem that yields concerns about their reliability in clinical applications in particular. Specifically, the reproducibility of biological markers across clinical datasets and distribution shifts across classes (e.g., healthy and disordered brains) is of paramount importance in revealing the underpinning mechanisms of diseases as well as propelling the development of personalized treatment. Motivated by these issues, we propose, for the first time, reproducibility-based GNN selection (RG-Select), a framework for GNN reproducibility assessment via the quantification of the most discriminative features (i.e., biomarkers) shared between different models. To ascertain the soundness of our framework, the reproducibility assessment embraces variations of different factors such as training strategies and data perturbations. Despite these challenges, our framework successfully yielded replicable conclusions across different training strategies and various clinical datasets. Our findings could thus pave the way for the development of biomarker trustworthiness and reliability assessment methods for computer-aided diagnosis and prognosis tasks. RG-Select code is available on GitHub at https://github.com/basiralab/RG-Select.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
