A 3D deep learning classifier and its explainability when assessing
  coronary artery disease
Wing Keung Cheung Jeremy Kalindjian Robert Bell Arjun Nair Leon J. Menezes Riyaz Patel Simon Wan Kacy Chou Jiahang Chen Ryo Torii Rhodri H. Davies James C. Moon Daniel C. Alexander Joseph Jacob
Early detection and diagnosis of coronary artery disease (CAD) could save
lives and reduce healthcare costs. In this study, we propose a 3D Resnet-50
deep learning model to directly classify normal subjects and CAD patients on
computed tomography coronary angiography images. Our proposed method
outperforms a 2D Resnet-50 model by 23.65%. Explainability is also provided by
using a Grad-GAM. Furthermore, we link the 3D CAD classification to a 2D
two-class semantic segmentation for improved explainability and accurate
abnormality localisation.