在眼科图像中，传统机器学习可达到与深度学习相当的异常检测效果。
A Comparative Study of Machine Learning and Deep Learning for Out-of-Distribution Detection
- 用传统机器学习和深度学习直接比较异常图像检测性能
- 两者在多个数据集上均实现1.000的AUROC和接近100%准确率
- 传统方法速度更快，更适合实际医疗场景部署
分布外（OOD）检测对构建可靠AI系统至关重要，因模型对无效输入产生的输出不可信。尽管深度学习通常被认为优于传统机器学习，但医学影像数据多遵循标准化采集协议，导致视觉变化有限，这促使我们在该场景下直接比较两类方法。研究在包含超过6万张视网膜与非视网膜图像的公开数据集上评估了两种方法，覆盖多种分辨率。两者在内部和外部验证集上均达到1.000的AUROC，准确率介于0.999至1.000之间，表现出相当的检测性能。然而，传统机器学习方法展现出显著更低的端到端延迟，同时保持同等准确率，表明其具有更高的计算效率。结果表明，在视觉复杂度较低的分布外检测任务中，轻量级传统机器学习方法可实现与深度学习相当的性能，且计算成本大幅降低，有利于实际部署。
原文摘要 · Abstract (English)
Out-of-distribution (OOD) detection is essential for building reliable AI systems, as models that produce outputs for invalid inputs cannot be trusted. Although deep learning (DL) is often assumed to outperform traditional machine learning (ML), medical imaging data are typically acquired under standardized protocols, leading to relatively constrained image variability in OOD detection tasks. This motivates a direct comparison between ML and DL approaches in this setting. The two approaches are evaluated on open datasets comprising over 60,000 fundus and non-fundus images across multiple resolutions. Both approaches achieved an AUROC of 1.000 and accuracies between 0.999 and 1.000 on internal and external validation sets, showing comparable detection performance. The ML approach, however, exhibited substantially lower end-to-end latency while maintaining equivalent accuracy, indicating greater computational efficiency. These results suggest that for OOD detection tasks of limited visual complexity, lightweight ML approaches can achieve DL-level performance with significantly reduced computational cost, supporting practical real-world deployment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。