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Radiomics is a term which refers to the analysis of the large amount ofquantitative tumor features extracted from medical images to find usefulpredictive, diagnostic or prognostic information. Many recent studies haveproved that radiomics can offer a lot of useful information that physicianscannot extract from the medical images and can be associated with otherinformation like gene or protein data. However, most of the classificationstudies in radiomics report the use of feature selection methods withoutidentifying the machine learning challenges behind radiomics. In this paper, wefirst show that the radiomics problem should be viewed as an high dimensional,low sample size, multi view learning problem, then we compare differentsolutions proposed in multi view learning for classifying radiomics data. Ourexperiments, conducted on several real world multi view datasets, show that theintermediate integration methods work significantly better than filter andembedded feature selection methods commonly used in radiomics.

Dissimilarity-based representation for radiomics applications