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.

翻译：在无创医学影像领域，放射组学特征被用于量化肿瘤特性。然而，这些特征可能受到图像离散化技术的影响，最终影响诊断的准确性。为探究不同图像离散化方法对诊断的影响，通常需要分别评估多种离散化策略。这种做法往往导致重复且耗时的任务，例如分别训练预测模型和微调超参数。本研究探讨了采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。研究结果表明，与单任务方法相比，多任务贝叶斯优化能显著加速超参数搜索过程。据我们所知，这是首次在关键医疗场景中应用多任务贝叶斯优化的研究。