Invasiveness Prediction of Pulmonary Adenocarcinomas Using Deep Feature Fusion Networks
This work addresses early diagnosis for lung cancer patients, but it is incremental as it combines existing feature types rather than introducing a new paradigm.
The study tackled the problem of predicting invasiveness in pulmonary adenocarcinomas from CT imaging by fusing radiomics and deep-learning features, resulting in improved prediction results as demonstrated on a dataset of 676 patients.
Early diagnosis of pathological invasiveness of pulmonary adenocarcinomas using computed tomography (CT) imaging would alter the course of treatment of adenocarcinomas and subsequently improve the prognosis. Most of the existing systems use either conventional radiomics features or deep-learning features alone to predict the invasiveness. In this study, we explore the fusion of the two kinds of features and claim that radiomics features can be complementary to deep-learning features. An effective deep feature fusion network is proposed to exploit the complementarity between the two kinds of features, which improves the invasiveness prediction results. We collected a private dataset that contains lung CT scans of 676 patients categorized into four invasiveness types from a collaborating hospital. Evaluations on this dataset demonstrate the effectiveness of our proposal.