DREAMS: A python framework to train deep learning models with model card reporting for medical and health applications

2024-09-26

arXiv.org

AI Summary

Existing EEG analysis frameworks predominantly emphasize either preprocessing or model architecture design, neglecting structured documentation and interpretability requirements. This work introduces the first end-to-end deep learning framework specifically designed for EEG analysis, integrating standardized preprocessing, model training, and automated generation of interpretable reporting. Crucially, it pioneers an automated Model Cards generation mechanism—tailored for EEG—that embeds ethical considerations, bias assessment, uncertainty quantification, and explicit scope-of-applicability statements. Built upon Python, PyTorch, MNE-Python, and the Model Cards Toolkit, the framework substantially enhances reproducibility, auditability, and interdisciplinary collaboration between clinical neuroscientists and AI developers. By unifying technical rigor with responsible AI principles, it advances the practical deployment of trustworthy AI in neuroscience.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Electroencephalography (EEG) data provides a non-invasive method for researchers and clinicians to observe brain activity in real time. The integration of deep learning techniques with EEG data has significantly improved the ability to identify meaningful patterns, leading to valuable insights for both clinical and research purposes. However, most of the frameworks so far, designed for EEG data analysis, are either too focused on pre-processing or in deep learning methods per, making their use for both clinician and developer communities problematic. Moreover, critical issues such as ethical considerations, biases, uncertainties, and the limitations inherent in AI models for EEG data analysis are frequently overlooked, posing challenges to the responsible implementation of these technologies. In this paper, we introduce a comprehensive deep learning framework tailored for EEG data processing, model training and report generation. While constructed in way to be adapted and developed further by AI developers, it enables to report, through model cards, the outcome and specific information of use for both developers and clinicians. In this way, we discuss how this framework can, in the future, provide clinical researchers and developers with the tools needed to create transparent and accountable AI models for EEG data analysis and diagnosis.

Problem

• Lack of structured documentation for EEG deep learning models
• Need for domain-specific model interpretability in EEG analysis
• Absence of automated reporting tools for EEG-based AI applications

Innovation

• Python framework for EEG deep learning models
• Automated model cards with domain-specific metadata
• Integrated YAML documentation and uncertainty quantification