对比三款深度学习框架在血细胞图像分类中的表现
Performance comparison of medical image classification systems using TensorFlow Keras, PyTorch, and JAX
- 用TensorFlow/Keras、PyTorch、JAX分别训练血细胞分类模型
- JAX和PyTorch在准确率上接近当前基准，推理速度受图像尺寸影响
- 适合关注医疗图像推理效率的研究者与开发者
医学影像在疾病早期诊断与监测中至关重要。血液显微镜检查能提供血细胞形态信息，辅助检测血液系统疾病。近年来，基于深度学习的自动化分类系统在提升血细胞图像分析的准确性和效率方面展现出巨大潜力。然而，针对具体深度学习框架的详细性能分析仍显不足。本文对比了三种主流深度学习框架——TensorFlow with Keras、PyTorch 和 JAX——在公开数据集 BloodMNIST 上进行血细胞图像分类的表现。研究重点在于推理时间差异，同时也考察了不同图像尺寸下的分类性能。结果表明，各框架表现存在差异，受图像分辨率及框架自身优化策略影响。JAX 与 PyTorch 的分类准确率与现有基准相当，显示出其在医学图像分类任务中的高效性。
原文摘要 · Abstract (English)
Medical imaging plays a vital role in early disease diagnosis and monitoring. Specifically, blood microscopy offers valuable insights into blood cell morphology and the detection of hematological disorders. In recent years, deep learning-based automated classification systems have demonstrated high potential in enhancing the accuracy and efficiency of blood image analysis. However, a detailed performance analysis of specific deep learning frameworks appears to be lacking. This paper compares the performance of three popular deep learning frameworks, TensorFlow with Keras, PyTorch, and JAX, in classifying blood cell images from the publicly available BloodMNIST dataset. The study primarily focuses on inference time differences, but also classification performance for different image sizes. The results reveal variations in performance across frameworks, influenced by factors such as image resolution and framework-specific optimizations. Classification accuracy for JAX and PyTorch was comparable to current benchmarks, showcasing the efficiency of these frameworks for medical image classification.
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