  Idiopathic pulmonary fibrosis (IPF) is a restrictive interstitial lung
disease that causes lung function decline by lung tissue scarring. Although
lung function decline is assessed by the forced vital capacity (FVC),
determining the accurate progression of IPF remains a challenge. To address
this challenge, we proposed Fibro-CoSANet, a novel end-to-end multi-modal
learning-based approach, to predict the FVC decline. Fibro-CoSANet utilized CT
images and demographic information in convolutional neural network frameworks
with a stacked attention layer. Extensive experiments on the OSIC Pulmonary
Fibrosis Progression Dataset demonstrated the superiority of our proposed
Fibro-CoSANet by achieving the new state-of-the-art modified Laplace
Log-Likelihood score of -6.68. This network may benefit research areas
concerned with designing networks to improve the prognostic accuracy of IPF.
The source-code for Fibro-CoSANet is available at:
\url{https://github.com/zabir-nabil/Fibro-CoSANet}.
