Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics:
  Application to the Early Detection of Pancreatic Cancer
Rebeca Vétil Clément Abi-Nader Alexandre Bône Marie-Pierre Vullierme Marc-Michel Rohé Pietro Gori Isabelle Bloch
We address the problem of learning Deep Learning Radiomics (DLR) that are not
redundant with Hand-Crafted Radiomics (HCR). To do so, we extract DLR features
using a VAE while enforcing their independence with HCR features by minimizing
their mutual information. The resulting DLR features can be combined with
hand-crafted ones and leveraged by a classifier to predict early markers of
cancer. We illustrate our method on four early markers of pancreatic cancer and
validate it on a large independent test set. Our results highlight the value of
combining non-redundant DLR and HCR features, as evidenced by an improvement in
the Area Under the Curve compared to baseline methods that do not address
redundancy or solely rely on HCR features.