测试三种预训练模型在噪声下的骨折检测能力，助力医疗公平
Pre-trained Under Noise: A Framework for Robust Bone Fracture Detection in Medical Imaging
- 用噪声模拟设备劣化，测试ResNet50等模型在劣质影像中的表现
- 模型准确率随噪声增强显著下降，其中EfficientNetv2相对更稳定
- 为发展适配低资源环境的AI诊断工具提供实证框架，适合医疗AI研究者
医学影像对骨骼疾病尤其是骨折的诊断至关重要。本文研究预训练深度学习模型在X光图像中识别骨折的鲁棒性，并试图通过技术缓解全球医疗资源不均问题。选取ResNet50、VGG16和EfficientNetv2三种预训练架构，在逐步添加噪声的条件下评估其性能。实验系统分析噪声对骨折检测的影响，以及不同模型在图像质量退化时的性能变化。该研究建立了一种基于迁移学习与可控噪声增强的方法论框架，用于评估AI模型在真实世界医疗场景中的退化情况。结果揭示了不同预训练模型在多样化环境中的泛化能力差异，为开发更具鲁棒性的医疗影像AI系统提供了实践指导。
原文摘要 · Abstract (English)
Medical Imagings are considered one of the crucial diagnostic tools for different bones-related diseases, especially bones fractures. This paper investigates the robustness of pre-trained deep learning models for classifying bone fractures in X-ray images and seeks to address global healthcare disparity through the lens of technology. Three deep learning models have been tested under varying simulated equipment quality conditions. ResNet50, VGG16 and EfficientNetv2 are the three pre-trained architectures which are compared. These models were used to perform bone fracture classification as images were progressively degraded using noise. This paper specifically empirically studies how the noise can affect the bone fractures detection and how the pre-trained models performance can be changes due to the noise that affect the quality of the X-ray images. This paper aims to help replicate real world challenges experienced by medical imaging technicians across the world. Thus, this paper establishes a methodological framework for assessing AI model degradation using transfer learning and controlled noise augmentation. The findings provide practical insight into how robust and generalizable different pre-trained deep learning powered computer vision models can be when used in different contexts.
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