融合深度学习与NLP，提升肺部疾病多标签分类准确率
Multilabel Classification for Lung Disease Detection: Integrating Deep Learning and Natural Language Processing
- 用RadGraph解析报告，自动提取标注信息增强模型
- 在CheXpert数据集上达F1 0.69、AUROC 0.86
- 适合医学影像分析、辅助放射科诊断的研究者
胸部X光片分类对经验丰富的放射科医生来说仍耗时且具挑战性，尤其在区分胸腔积液、气胸和肺炎等病症时。本文提出一种新型迁移学习模型，用于多标签肺部疾病分类，基于包含超过12,617张前位胸片的CheXpert数据集进行分析。通过引入RadGraph解析技术高效提取报告标注信息，显著提升了模型从复杂医学图像中准确识别多种肺部疾病的能力。该模型在测试中取得F1分数0.69和AUROC 0.86，展现出临床应用潜力。同时，利用自然语言处理（NLP）解析报告元数据，缓解疾病分类中的不确定性。通过对比模糊报告与明确病例，强化模型判断能力。本研究揭示了深度学习与NLP结合在放射学诊断中的协同价值，有助于提升胸部影像的高效分析水平。
原文摘要 · Abstract (English)
Classifying chest radiographs is a time-consuming and challenging task, even for experienced radiologists. This provides an area for improvement due to the difficulty in precisely distinguishing between conditions such as pleural effusion, pneumothorax, and pneumonia. We propose a novel transfer learning model for multi-label lung disease classification, utilizing the CheXpert dataset with over 12,617 images of frontal radiographs being analyzed. By integrating RadGraph parsing for efficient annotation extraction, we enhance the model's ability to accurately classify multiple lung diseases from complex medical images. The proposed model achieved an F1 score of 0.69 and an AUROC of 0.86, demonstrating its potential for clinical applications. Also explored was the use of Natural Language Processing (NLP) to parse report metadata and address uncertainties in disease classification. By comparing uncertain reports with more certain cases, the NLP-enhanced model improves its ability to conclusively classify conditions. This research highlights the connection between deep learning and NLP, underscoring their potential to enhance radiological diagnostics and aid in the efficient analysis of chest radiographs.
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