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

TL;DR

This paper introduces DE-CGAN, a novel generative model that enhances data diversity by creating synthetic examples for underrepresented cases, improving rTMS treatment outcome prediction accuracy.

Contribution

The paper proposes DE-CGAN, a new diversity-enhancing conditional GAN that oversamples difficult-to-classify data points, outperforming traditional augmentation and benchmarks.

Findings

DE-CGAN improves classification performance over traditional methods.

Synthetic data increases robustness of treatment outcome predictions.

Enhanced datasets facilitate better understanding of variable relationships.

Abstract

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.