融合ViT与Swin Transformer，92.06%准确率实现14类肺病分类
Developing a Dual-Stage Vision Transformer Model for Lung Disease Classification
- 双阶段设计：先用ViT提取全局特征，再用Swin Transformer捕捉局部细节
- 在未见测试集上达到92.06%的标签级准确率
- 适合医疗AI研发者及放射科医生参考应用
肺疾病在美国已影响超3400万人，及时准确诊断至关重要。本文构建了一种双阶段视觉变换器模型，结合视觉变换器（ViT）与Swin Transformer，从患者胸片中分类14种不同肺疾病。经数据预处理并训练神经网络后，该模型在未见测试子集上实现了92.06%的标签级准确率，展现出对肺疾病精准分类与患者诊断的潜力。
原文摘要 · Abstract (English)
Lung diseases have become a prevalent problem throughout the United States, affecting over 34 million people. Accurate and timely diagnosis of the different types of lung diseases is critical, and Artificial Intelligence (AI) methods could speed up these processes. A dual-stage vision transformer is built throughout this research by integrating a Vision Transformer (ViT) and a Swin Transformer to classify 14 different lung diseases from X-ray scans of patients with these diseases. The proposed model achieved an accuracy of 92.06% on a label-level when making predictions on an unseen testing subset of the dataset after data preprocessing and training the neural network. The model showed promise for accurately classifying lung diseases and diagnosing patients who suffer from these harmful diseases.
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