Summary of Dreams: a Python Framework to Train Deep Learning Models with Model Card Reporting For Medical and Health Applications, by Rabindra Khadka et al.
DREAMS: A python framework to train deep learning models with model card reporting for medical and health applications
by Rabindra Khadka, Pedro G Lind, Anis Yazidi, Asma Belhadi
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a comprehensive deep learning framework for processing, training, and reporting on electroencephalography (EEG) data. This framework is designed to be adaptable by both clinicians and developers, enabling the creation of transparent and accountable AI models for EEG data analysis and diagnosis. The integration of deep learning techniques with EEG data has improved pattern identification, providing valuable insights for clinical and research purposes. However, most frameworks are either too focused on pre-processing or deep learning methods, making them problematic for both communities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is important because it provides a comprehensive framework for analyzing EEG data using AI models. This will enable clinicians to make more accurate diagnoses and researchers to gain valuable insights into brain activity. The framework includes model cards that provide information about the outcome and specific details of use, making it easier for developers and clinicians to work together. |
Keywords
» Artificial intelligence » Deep learning
