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We address the problem of learning Deep Learning Radiomics (DLR) that are notredundant with Hand-Crafted Radiomics (HCR). To do so, we extract DLR featuresusing a VAE while enforcing their independence with HCR features by minimizingtheir mutual information. The resulting DLR features can be combined withhand-crafted ones and leveraged by a classifier to predict early markers ofcancer. We illustrate our method on four early markers of pancreatic cancer andvalidate it on a large independent test set. Our results highlight the value ofcombining non-redundant DLR and HCR features, as evidenced by an improvement inthe Area Under the Curve compared to baseline methods that do not addressredundancy or solely rely on HCR features.

Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer