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

arXiv:2404.16913v1 Announce Type: new
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 …

abstract adversarial artificial artificial intelligence arxiv boosting connectivity cs.ai cs.lg depression diversity eess.iv evidence features fmri generative generative adversarial networks however intelligence networks patients patterns prediction treatment type while