DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks
arxiv(2024)
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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