One-Hot Logistic Regression for Radiomics-Based Classification

Radiomics maps digital medical images into quantitative data with the end goal of generating imaging biomarkers as decision support tools for clinical practice. Radiomics features are extracted from biomedical images and then process with machine learning algorithms. Logistic regression is a very common machine learning technique to investigate radiomics features but its linearity limits its performance. In this paper, we propose a non-linear logistic regression based on features binning. It is shown that this non-linear regression coincides with the naive Bayes classifier. Furthermore, it is shown that each radiomics feature is associated with a reliable feature profile. This profile allows us to interpret the importance of the feature in the original biomedical image. We illustrate our theoretical results with two radiomics datasets.