A new paper published on arXiv explores the critical need for uncertainty quantification (UQ) in graph learning models applied to connectomics. The research highlights that while models like Graph Attention Networks (GATs) can achieve high diagnostic accuracy, they often exhibit severe overconfidence in their predictions. A case study using the SUDMEX CONN dataset for cocaine dependence classification demonstrated this issue, where misclassified subjects received high confidence scores. The paper argues that rigorous UQ and calibration mechanisms are essential for developing trustworthy biomarkers in clinical neuroscience. AI
IMPACT Highlights the need for improved reliability and trustworthiness in AI models used for clinical neuroscience diagnostics.
RANK_REASON The cluster contains a research paper published on arXiv detailing a narrative review and case study on uncertainty quantification in graph learning for connectomics. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- cocaine dependence
- Graph Attention Network
- graph neural networks
- Hugging Face
- Mansooreh Pakravan
- Monte Carlo Dropout
- SUDMEX CONN
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