  Pulmonary fibrosis is a devastating chronic lung disease that causes
irreparable lung tissue scarring and damage, resulting in progressive loss in
lung capacity and has no known cure. A critical step in the treatment and
management of pulmonary fibrosis is the assessment of lung function decline,
with computed tomography (CT) imaging being a particularly effective method for
determining the extent of lung damage caused by pulmonary fibrosis. Motivated
by this, we introduce Fibrosis-Net, a deep convolutional neural network design
tailored for the prediction of pulmonary fibrosis progression from chest CT
images. More specifically, machine-driven design exploration was leveraged to
determine a strong architectural design for CT lung analysis, upon which we
build a customized network design tailored for predicting forced vital capacity
(FVC) based on a patient's CT scan, initial spirometry measurement, and
clinical metadata. Finally, we leverage an explainability-driven performance
validation strategy to study the decision-making behaviour of Fibrosis-Net as
to verify that predictions are based on relevant visual indicators in CT
images. Experiments using a patient cohort from the OSIC Pulmonary Fibrosis
Progression Challenge showed that the proposed Fibrosis-Net is able to achieve
a significantly higher modified Laplace Log Likelihood score than the winning
solutions on the challenge. Furthermore, explainability-driven performance
validation demonstrated that the proposed Fibrosis-Net exhibits correct
decision-making behaviour by leveraging clinically-relevant visual indicators
in CT images when making predictions on pulmonary fibrosis progress. While
Fibrosis-Net is not yet a production-ready clinical assessment solution, we
hope that its release in open source manner will encourage researchers,
clinicians, and citizen data scientists alike to leverage and build upon it.
