用多任务贝叶斯优化加速肺结节良恶性分类的SVM调参。
Alleviating Hyperparameter-Tuning Burden in SVM Classifiers for Pulmonary Nodules Diagnosis with Multi-Task Bayesian Optimization
- 通过多任务贝叶斯优化并行搜索多个图像离散化方法的SVM超参数。
- 相比单任务方法，超参搜索速度提升显著，且保持诊断准确率。
- 首次在肺结节诊断中应用多任务贝叶斯优化，适合医疗影像研究者。
在非侵入性医学影像领域，影像组学特征被用于量化肿瘤特性。然而，这些特征易受图像离散化技术影响，进而影响诊断准确性。为评估不同图像离散化方法的影响，通常需逐一测试多种策略，导致重复的模型训练与超参数调优，耗时耗力。本研究探讨利用多任务贝叶斯优化加速基于RBF-SVM的肺结节良恶性分类超参数搜索的可行性。结果表明，与单任务方法相比，多任务贝叶斯优化显著加快了超参数搜索过程。据我们所知，这是首个将多任务贝叶斯优化应用于关键医疗场景的研究。
原文摘要 · Abstract (English)
In the field of non-invasive medical imaging, radiomic features are utilized to measure tumor characteristics. However, these features can be affected by the techniques used to discretize the images, ultimately impacting the accuracy of diagnosis. To investigate the influence of various image discretization methods on diagnosis, it is common practice to evaluate multiple discretization strategies individually. This approach often leads to redundant and time-consuming tasks such as training predictive models and fine-tuning hyperparameters separately. This study examines the feasibility of employing multi-task Bayesian optimization to accelerate the hyperparameters search for classifying benign and malignant pulmonary nodules using RBF SVM. Our findings suggest that multi-task Bayesian optimization significantly accelerates the search for hyperparameters in comparison to a single-task approach. To the best of our knowledge, this is the first investigation to utilize multi-task Bayesian optimization in a critical medical context.
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