复现并改进了胸部X光疾病分类模型CheXNet，提升诊断性能。
Reproducing and Improving CheXNet: Deep Learning for Chest X-ray Disease Classification
- 基于NIH ChestX-ray14数据集复现并优化了CheXNet模型
- 最佳模型平均AUC-ROC达0.85，平均F1得分为0.39
- 适用于医学影像多标签分类任务的性能评估与对比
深度学习在放射影像分析领域快速发展，有望成为现代医学的标准实践。针对公开的NIH ChestX-ray14数据集（包含14种疾病的胸部X光片），本文复现了名为CheXNet的算法，并探索了表现优于其基线指标的其他算法。模型性能主要通过F1分数和AUC-ROC进行评估，这两项指标对医学影像中不平衡的多标签分类任务至关重要。最优模型在所有14种疾病分类上的平均AUC-ROC达到0.85，平均F1得分为0.39。
原文摘要 · Abstract (English)
Deep learning for radiologic image analysis is a rapidly growing field in biomedical research and is likely to become a standard practice in modern medicine. On the publicly available NIH ChestX-ray14 dataset, containing X-ray images that are classified by the presence or absence of 14 different diseases, we reproduced an algorithm known as CheXNet, as well as explored other algorithms that outperform CheXNet's baseline metrics. Model performance was primarily evaluated using the F1 score and AUC-ROC, both of which are critical metrics for imbalanced, multi-label classification tasks in medical imaging. The best model achieved an average AUC-ROC score of 0.85 and an average F1 score of 0.39 across all 14 disease classifications present in the dataset.
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