arXiv 2404.169132024-04-25cs.LGcs.AIeess.IV

DE-CGAN：利用多样性增强条件生成对抗网络提升 rTMS 治疗预测

DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks

University of Southern Queensland（南昆士兰大学）；Belmont Private Hospital（贝尔蒙特私立医院）；Lingnan University（岭南大学）；Queensland University of Technology（昆士兰科技大学）

Matthew Squires, Xiaohui Tao, Soman Elangovan, Raj Gururajan, Haoran Xie, Xujuan Zhou, Yuefeng Li, U Rajendra Acharya

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AI总结：

针对 rTMS 治疗中代表性不足的 fMRI 数据导致 DNN 预测不可靠的问题，提出 DE-CGAN 方法通过生成合成样本增强数据多样性，显著提升了分类模型性能。

摘要：

重复经颅磁刺激是一种得到充分支持且基于证据的抑郁症治疗方法。然而，对该治疗的反应模式并不一致。新兴证据表明，人工智能可以使用 fMRI 连接特征预测大多数患者的 rTMS 治疗结果。虽然这些模型能够可靠地预测许多患者的治疗结果，但对于一些代表性不足的 fMRI 连接性测量，DNN 模型无法可靠地预测治疗结果。因此，我们提出了一种新方法，即多样性增强条件生成对抗网络（DE-CGAN），用于对这些代表性不足的样本进行过采样。DE-CGAN 首先识别难以分类的数据点，然后创建条件合成样本以增强数据多样性，从而在难以分类的区域生成合成样本。通过实证实验，我们表明使用多样性增强训练集训练的分类模型优于传统的数据增强技术和现有的基准结果。这项工作表明，增加训练数据集的多样性可以提高分类模型的性能。此外，这项工作为合成患者的实用性提供了证据，为 AI 研究人员和精神病学家提供了更大、更稳健的数据集，以探索变量之间的关系。

英文摘要：

Repetitive Transcranial Magnetic Stimulation (rTMS) is a well-supported, evidence-based treatment for depression. However, patterns of response to this treatment are inconsistent. Emerging evidence suggests that artificial intelligence can predict rTMS treatment outcomes for most patients using fMRI connectivity features. While these models can reliably predict treatment outcomes for many patients for some underrepresented fMRI connectivity measures DNN models are unable to reliably predict treatment outcomes. As such we propose a novel method, Diversity Enhancing Conditional General Adversarial Network (DE-CGAN) for oversampling these underrepresented examples. DE-CGAN creates synthetic examples in difficult-to-classify regions by first identifying these data points and then creating conditioned synthetic examples to enhance data diversity. Through empirical experiments we show that a classification model trained using a diversity enhanced training set outperforms traditional data augmentation techniques and existing benchmark results. This work shows that increasing the diversity of a training dataset can improve classification model performance. Furthermore, this work provides evidence for the utility of synthetic patients providing larger more robust datasets for both AI researchers and psychiatrists to explore variable relationships.

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