Alleviating Hyperparameter-Tuning Burden in SVM Classifiers for Pulmonary Nodules Diagnosis with Multi-Task Bayesian Optimization

TL;DR

This paper introduces a multi-task Bayesian optimization method to efficiently tune hyperparameters of SVM classifiers, significantly reducing the time needed for pulmonary nodule diagnosis in medical imaging.

Contribution

It is the first to apply multi-task Bayesian optimization in a medical diagnosis context, improving hyperparameter tuning efficiency for SVM classifiers.

Findings

Multi-task Bayesian optimization accelerates hyperparameter search.

Significant reduction in tuning time compared to single-task methods.

First application of this approach in a critical medical diagnosis task.

Abstract

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.