对比Keras、PyTorch和JAX在病理图像分类中的表现
Comparative Analysis of CNN Performance in Keras, PyTorch and JAX on PathMNIST
- 用PathMNIST数据集比较三框架的CNN实现性能
- 发现计算速度与模型准确率存在权衡关系
- 适合医疗影像分析的研究者选择开发工具
深度学习显著推动了医学图像分类的发展，尤其是卷积神经网络（CNN）的应用。Keras、PyTorch和JAX等深度学习框架在模型开发与部署中各有优势，但其在医学影像任务中的对比性能仍缺乏系统研究。本研究以PathMNIST数据集为基准，全面分析了三种框架上CNN实现的训练效率、分类准确率和推理速度，评估其在真实应用场景中的适用性。结果揭示了计算速度与模型精度之间的权衡，为医学图像分析领域的研究人员和实践者提供了重要参考。
原文摘要 · Abstract (English)
Deep learning has significantly advanced the field of medical image classification, particularly with the adoption of Convolutional Neural Networks (CNNs). Various deep learning frameworks such as Keras, PyTorch and JAX offer unique advantages in model development and deployment. However, their comparative performance in medical imaging tasks remains underexplored. This study presents a comprehensive analysis of CNN implementations across these frameworks, using the PathMNIST dataset as a benchmark. We evaluate training efficiency, classification accuracy and inference speed to assess their suitability for real-world applications. Our findings highlight the trade-offs between computational speed and model accuracy, offering valuable insights for researchers and practitioners in medical image analysis.
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