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

Fibro-CoSANet: Pulmonary Fibrosis Prognosis Prediction using a Convolutional Self Attention Network

“…As IPF is associated with demographics, such as baseline FVC [2,13], age [14,13], gender [14], smoking status [14], we take inspiration from [9] to incorporate these features along with CT image to improve the performance of our proposed method. We normalized the estimated lung volume, age, sex, and smoking status features using:…”

“…The recent advancements of artificial intelligence (e.g., convolutional neural networks (CNNs) [7]) and the Kaggle: OSIC Pulmonary Fibrosis Progression Challenge [8] 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 [9] 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 [9].…”

“…In particular, Wong et al [9] 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 [9]. While the existing CNNs based approaches have a higher capacity to predict pulmonary fibrosis progression from chest CT images, we strongly believe there is still room for improvement in terms of overall correctness.…”

“…The merged features were then passed through a one-layer perceptron to obtain the predicted FVC. While the Fibrosis-Net [9] utilized the multiple CT slices to generate convolutional features, we introduced an efficient formulation of the IPF prognosis task where we randomly selected a single CT image from multiple scans to extract convolutional features. However, we used the approximated lung volume information from all the available scans as a shallow feature which was merged with the convolutional features.…”

“…However, we used the approximated lung volume information from all the available scans as a shallow feature which was merged with the convolutional features. In addition, we predicted the slope of FVC based on a linear prior assumption to reduce the computational overhead, while Fibrosis-Net [9] used an elastic net to obtain the local FVCs.…”