Artificial Intelligence and Digital Health Research Group, Singapore Eye Research Institute, Singapore, Singapore.
Artificial Intelligence Office, Singapore Health Service, Singapore, Singapore.
Sci Rep. 2024 May 7;14(1):10483. doi: 10.1038/s41598-024-60429-4.
Automated machine learning (AutoML) allows for the simplified application of machine learning to real-world problems, by the implicit handling of necessary steps such as data pre-processing, feature engineering, model selection and hyperparameter optimization. This has encouraged its use in medical applications such as imaging. However, the impact of common parameter choices such as the number of trials allowed, and the resolution of the input images, has not been comprehensively explored in existing literature. We therefore benchmark AutoKeras (AK), an open-source AutoML framework, against several bespoke deep learning architectures, on five public medical datasets representing a wide range of imaging modalities. It was found that AK could outperform the bespoke models in general, although at the cost of increased training time. Moreover, our experiments suggest that a large number of trials and higher resolutions may not be necessary for optimal performance to be achieved.
自动化机器学习（AutoML）通过自动处理数据预处理、特征工程、模型选择和超参数优化等必要步骤，简化了机器学习在实际问题中的应用。这鼓励了它在医学成像等应用中的使用。然而，在现有文献中，并没有全面探讨常见参数选择（如允许的试验次数和输入图像的分辨率）对 AutoML 的影响。因此，我们使用五个公共医学数据集，对 AutoKeras（AK）这一开源的 AutoML 框架和几个定制的深度学习架构进行了基准测试，这些数据集代表了广泛的成像方式。结果发现，AK 通常可以优于定制模型，尽管训练时间会增加。此外，我们的实验表明，要实现最佳性能，不一定需要大量的试验和更高的分辨率。
