Dissimilarity-based representation for radiomics applications
This work addresses the challenge of improving classification accuracy in radiomics for medical applications, though it is incremental as it applies existing multi-view learning solutions to this domain.
The paper tackles the classification problem in radiomics by reframing it as a high-dimensional, low-sample-size, multi-view learning challenge, and finds that intermediate integration methods outperform commonly used feature selection techniques in experiments on real-world datasets.
Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognostic information. Many recent studies have proved that radiomics can offer a lot of useful information that physicians cannot extract from the medical images and can be associated with other information like gene or protein data. However, most of the classification studies in radiomics report the use of feature selection methods without identifying the machine learning challenges behind radiomics. In this paper, we first show that the radiomics problem should be viewed as an high dimensional, low sample size, multi view learning problem, then we compare different solutions proposed in multi view learning for classifying radiomics data. Our experiments, conducted on several real world multi view datasets, show that the intermediate integration methods work significantly better than filter and embedded feature selection methods commonly used in radiomics.