Respiratory Medicine, Royal Free London NHS Foundation Trust, London, UK Corresponding author: Dr Joseph Jacob UCL Centre for Medical Image Computing 1st Floor, 90 High Holborn, London WC1V6LJ j.jacob@ucl.ac.uk Abstract The morphology and distribution of airway tree abnormalities enables diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity. In this study, we propose a data-centric deep learning technique to segment the airway tree. The proposed technique utilises interpolation and image split to improve data usefulness and quality. Then, an ensemble learning strategy is implemented to aggregate the segmented airway trees at different scales. In terms of segmentation performance (dice similarity coefficient), our method outperforms the baseline model by 2.5% on average when a combined loss is used. Further, our proposed technique has a low GPU usage and high flexibility enabling it to be deployed on any 2D deep learning model. Introduction Abnormal dilatation of the airways is a key feature in the diagnosis of idiopathic pulmonary fibrosis (IPF) patients. Disease extent and severity in IPF can be assessed by visual analysis of high-resolution CT images by radiologists.

Corresponding author: Dr Joseph Jacob UCL Centre for Medical Image Computing 1st Floor, 90 High Holborn, London WC1V6LJ j.jacob@ucl.ac.uk Abstract 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. Introduction Coronary artery disease (CAD) is a common cause of death [1] in developed (i.e., UK, USA) and developing countries (i.e., India, Philippines). Early detection and diagnosis of CAD could save lives and costs [2]. Currently, computed tomography coronary angiography (CTCA) plays a central role in diagnosing or excluding CAD in patients with chest pain [3, 4].