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AI Research
arXiv CS.AI·29d ago·5 min read
PulmoSight-XAI: An Explainable Multi-View Attention Ensemble with Gradient Boosting Meta-Learning for Multi-Label Chest X-Ray Classification
TL;DR
PulmoSight-XAI introduces a novel explainable AI model for multi-label chest X-ray classification, utilizing a multi-view attention ensemble combined with gradient boosting meta-learning. This approach enhances interpretability while achieving high accuracy in identifying multiple conditions from X-ray images.
✦ Why It Matters
Radiologists can implement PulmoSight-XAI to enhance diagnostic accuracy and gain insights into AI decision-making in clinical settings.
Key Takeaways
- Utilize multi-view analysis for improved chest X-ray classification.
- Incorporate hybrid loss functions to address class imbalance.
- Leverage gradient-boosting models for enhanced prediction accuracy.
- Apply explainability techniques to ensure clinical relevance.
How It Works
PulmoSight-XAI employs a hierarchical multi-view ensemble framework that processes frontal and lateral chest X-rays separately. It uses five convolutional neural networks to capture diverse features, while a multi-scale feature fusion strategy, supported by Convolutional Block Attention Modules (CBAM), preserves detailed representations.
The framework optimizes model performance through a hybrid loss function that balances positive and negative samples, and integrates predictions from multiple models using a two-level meta-learning approach.