Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design For Prediction of Pulmonary Fibrosis Progression From Chest CT Images

Abstract

Background: 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.Results: 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. Conclusion: Fibrosis-Net is able to achieve a significantly higher modified Laplace Log Likelihood score than the winning solutions on the OSIC Pulmonary Fibrosis Progression Challenge, and has been shown to exhibit correct decision-making behaviour when making predictions. Fibrosis-Net is available to the general public in an open-source and open access manner as part of the OpenMedAI initiative. While Fibrosis-Net is not yet a production-ready clinical assessment solution, we hope that its release will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon it.

Insights into Data through Model Behaviour: An Explainability-driven Strategy for Data Auditing for Responsible Computer Vision Applications

“…The discovered data quality issues led to the following actionable insights: 1) incorrect calibration data removal, 2-3) domain-specific artifact mitigation, and 4) automatic table removal (see Figure 2). By taking the above actions on the data set to address the discovered data quality issues uncovered via the aforementioned explainability-driven strategy for data auditing, the resulting deep learning models not only achieved significantly higher performance [15,5], but also led to models that made predictions based on the right visual cues.…”

“…For example, in the case of the OSIC Pulmonary Fibrosis Progression dataset, addressing the discovered data quality issues led to the creation of a deep CNN regression model [15] with state-of-the-art performance above the winning solutions in the OSIC Kaggle Challenge [1] that learned to leverage relevant visual anomalies such as honeycombing in the lungs (see Fig. 4 for example CT images from the OSIC Pulmonary Fibrosis Progression dataset and corresponding identified critical factors).…”

Fibro-CoSANet: pulmonary fibrosis prognosis prediction using a convolutional self attention network

“…convolutional neural networks (CNNs) ) and the Kaggle: OSIC Pulmonary Fibrosis Progression Challenge (Kaggle) have significantly inspired to develop CT image based machine learning systems to obtain computer-aided clinical decision for IPF prognosis. In particular, Wong et al (2021) recently proposed Fibrosis-Net based on deep CNNs for predicting pulmonary fibrosis progression from chest CT images. Fibrosis-Net utilized the chest CT scans of a patient along with spirometry measurement and clinical metadata to predict the FVC of a patient at a specific time-point in the future (Wong et al 2021).…”