New Graph Transformer Model Achieves High Accuracy in Lung Inflammation Classification

A research paper introduces a novel deep learning architecture for classifying silicosis and pneumonia using chest X-rays. The approach integrates graph transformer networks with traditional deep neural networks and employs a Balanced Cross-Entropy loss function. An ensemble of these models achieved a macro-F1 score of 0.9749 and AUC ROC scores over 0.99 for each class on a newly curated dataset named SVBCX. AI

RANK_REASON The cluster contains a withdrawn academic paper detailing a novel deep learning architecture and dataset for medical image classification. [lever_c_demoted from research: ic=1 ai=1.0]

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• Balanced Cross-Entropy
• Duy Le
• Pneumonia
• Silicosis
• SVBCX

• paper
• model release
• product

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