Sithin Thulasi Seetha

Abstract While considerable effort has been devoted to examining how variations in study protocols, acquisition settings, and annotations influence radiomics features, the role of feature selection (FS) methods largely remains unexplored. This study investigates a self-supervised deep sparse autoencoder ensemble (ensembleAE) and a novel Bayesian variant (bayesianAE) for radiomics FS in a small, class-imbalanced framework.

2.2. In Silico Contour Generation To evaluate the impact of segmentation variations on radiomic feature stability, we synthetically generated 15 new prostate ROIs for each patient. We synthesized these contours by subjecting the manual ROI segmentation to bounded perturbations using affine transformations. The transformations include shifting, scaling, and rotation to simulate under-/over-segmentation variations.