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.

翻译：我们研究了学习与手工影像组学（HCR）无冗余的深度学习影像组学（DLR）的问题。为此，我们使用变分自编码器（VAE）提取DLR特征，同时通过最小化其与HCR特征的互信息来强制二者相互独立。所得DLR特征可与手工特征结合，并由分类器用于预测癌症早期标志物。我们在四个胰腺癌早期标志物上验证了该方法，并在大规模独立测试集上进行了评估。结果表明，组合非冗余的DLR与HCR特征具有重要价值，相比未处理冗余问题或仅依赖HCR特征的基线方法，曲线下面积（AUC）显著提升。