Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer
This work addresses early detection of pancreatic cancer, which is incremental as it combines existing feature types with a new redundancy-reduction method.
The paper tackled the problem of learning deep learning radiomics (DLR) that are not redundant with hand-crafted radiomics (HCR) for early pancreatic cancer detection, resulting in improved Area Under the Curve compared to baseline methods.
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.