Adaptive Fusion of Radiomics and Deep Features for Lung Adenocarcinoma
  Subtype Recognition
Jing Zhou Xiaotong Fu Xirong Li Wei Feng Zhang Zhang Ying Ji
The most common type of lung cancer, lung adenocarcinoma (LUAD), has been
increasingly detected since the advent of low-dose computed tomography
screening technology. In clinical practice, pre-invasive LUAD (Pre-IAs) should
only require regular follow-up care, while invasive LUAD (IAs) should receive
immediate treatment with appropriate lung cancer resection, based on the cancer
subtype. However, prior research on diagnosing LUAD has mainly focused on
classifying Pre-IAs/IAs, as techniques for distinguishing different subtypes of
IAs have been lacking. In this study, we proposed a multi-head attentional
feature fusion (MHA-FF) model for not only distinguishing IAs from Pre-IAs, but
also for distinguishing the different subtypes of IAs. To predict the subtype
of each nodule accurately, we leveraged both radiomics and deep features
extracted from computed tomography images. Furthermore, those features were
aggregated through an adaptive fusion module that can learn attention-based
discriminative features. The utility of our proposed method is demonstrated
here by means of real-world data collected from a multi-center cohort.