Innovative Silicosis and Pneumonia Classification: Leveraging Graph Transformer Post-hoc Modeling and Ensemble Techniques

2024-12-31

AI Summary

This study addresses the challenging task of precise differential diagnosis between silicosis and pneumonia from chest X-ray (CXR) images. To this end, we introduce SVBCX—the first etiology-specific CXR dataset explicitly designed for silicosis versus pneumonia classification. We propose a hybrid Graph Transformer–CNN architecture: CNNs extract local textural features, while the Graph Transformer models anatomical structural relationships within lung regions and enables posterior reasoning. A Balanced Cross-Entropy loss mitigates severe class imbalance, and multi-model ensemble enhances robustness. Key contributions include: (1) the novel SVBCX dataset; (2) a graph-structure-guided Transformer–CNN fusion paradigm; and (3) a discriminative enhancement mechanism tailored for small-sample pathological classes. On SVBCX, our method achieves a macro-F1 score of 0.9749 and AUC > 0.99 for all classes, significantly outperforming state-of-the-art baselines.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Interpretability, Explainability, and Transparency
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

This paper presents a comprehensive study on the classification and detection of Silicosis-related lung inflammation. Our main contributions include 1) the creation of a newly curated chest X-ray (CXR) image dataset named SVBCX that is tailored to the nuances of lung inflammation caused by distinct agents, providing a valuable resource for silicosis and pneumonia research community; and 2) we propose a novel deep-learning architecture that integrates graph transformer networks alongside a traditional deep neural network module for the effective classification of silicosis and pneumonia. Additionally, we employ the Balanced Cross-Entropy (BalCE) as a loss function to ensure more uniform learning across different classes, enhancing the model's ability to discern subtle differences in lung conditions. The proposed model architecture and loss function selection aim to improve the accuracy and reliability of inflammation detection, particularly in the context of Silicosis. Furthermore, our research explores the efficacy of an ensemble approach that combines the strengths of diverse model architectures. Experimental results on the constructed dataset demonstrate promising outcomes, showcasing substantial enhancements compared to baseline models. The ensemble of models achieves a macro-F1 score of 0.9749 and AUC ROC scores exceeding 0.99 for each class, underscoring the effectiveness of our approach in accurate and robust lung inflammation classification.

Problem

• Silicosis
• Pneumonia
• Chest X-ray Classification

Innovation

• SVBCX Dataset
• Graph Transformation Network
• Balanced Cross Entropy