Radiomics-Informed Deep Learning for Classification of Atrial
  Fibrillation Sub-Types from Left-Atrium CT Volumes
Weihang Dai Xiaomeng Li Taihui Yu Di Zhao Jun Shen Kwang-Ting Cheng
Atrial Fibrillation (AF) is characterized by rapid, irregular heartbeats, and
can lead to fatal complications such as heart failure. The disease is divided
into two sub-types based on severity, which can be automatically classified
through CT volumes for disease screening of severe cases. However, existing
classification approaches rely on generic radiomic features that may not be
optimal for the task, whilst deep learning methods tend to over-fit to the
high-dimensional volume inputs. In this work, we propose a novel
radiomics-informed deep-learning method, RIDL, that combines the advantages of
deep learning and radiomic approaches to improve AF sub-type classification.
Unlike existing hybrid techniques that mostly rely on na\"ive feature
concatenation, we observe that radiomic feature selection methods can serve as
an information prior, and propose supplementing low-level deep neural network
(DNN) features with locally computed radiomic features. This reduces DNN
over-fitting and allows local variations between radiomic features to be better
captured. Furthermore, we ensure complementary information is learned by deep
and radiomic features by designing a novel feature de-correlation loss.
Combined, our method addresses the limitations of deep learning and radiomic
approaches and outperforms state-of-the-art radiomic, deep learning, and hybrid
approaches, achieving 86.9% AUC for the AF sub-type classification task. Code
is available at https://github.com/xmed-lab/RIDL.