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Abstract #4811

Target-class-agnostic feature rejection for radiomics analyses based on variations of tumor segmentation mask

Balthasar Schachtner1, Michael Ingrisch1, Gresser Eva1, Moritz Schneider1, Andrea Schreier1, Olga Solyanik1, Guiseppe Magistro2, and Dominik Nörenberg1

1Department of Radiology, Munich University Hospitals, LMU, Munich, Germany, 2Department of Urology, Munich University Hospitals, LMU, Munich, Germany

Feature selection is a key aspect to radiomics analyses. An approach to remove features which are not stable with respect to small variations of the segmented mask is presented. The rejection works target-class agnostic and can be used in combination with target-class-based selections. An increase of about 5 percentage points can be seen when using the proposed approach in a simple machine learning setup on prostate MRI of prostate cancer patients.

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