Quantitative CT analysis in ILD and use of artificial intelligence on imaging of ILD

Advances in computer technology over the past decade, particularly in the field of medical image analysis, have permitted the identification, characterisation and quantitation of abnormalities that can be used to diagnose disease or determine disease severity. On CT imaging performed in patients with ILD, deep-learning computer algorithms now demonstrate comparable performance with trained observers in the identification of a UIP pattern, which is associated with a poor prognosis in several fibrosing ILDs. Computer tools that quantify individual voxel-level CT features have also come of age and can predict mortality with greater power than visual CT analysis scores. As these tools become more established, they have the potential to improve the sensitivity with which minor degrees of disease progression are identified. Currently, PFTs are the gold standard measure used to assess clinical deterioration. However, the variation associated with pulmonary function measurements may mask the presence of small but genuine functional decline, which in the future could be confirmed by computer tools. The current chapter will describe the latest advances in quantitative CT analysis and deep learning as related to ILDs and suggest potential future directions for this rapidly advancing field

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Learning to detect chest radiographs containing lung nodules using visual attention networks

Machine learning approaches hold great potential for the automated detection of lung nodules in chest radiographs, but training the algorithms requires vary large amounts of manually annotated images, which are difficult to obtain. Weak labels indicating whether a radiograph is likely to contain pulmonary nodules are typically easier to obtain at scale by parsing historical free-text radiological reports associated to the radiographs. Using a repositotory of over 700,000 chest radiographs, in this study we demonstrate that promising nodule detection performance can be achieved using weak labels through convolutional neural networks for radiograph classification. We propose two network architectures for the classification of images likely to contain pulmonary nodules using both weak labels and manually-delineated bounding boxes, when these are available. Annotated nodules are used at training time to deliver a visual attention mechanism informing the model about its localisation performance. The first architecture extracts saliency maps from high-level convolutional layers and compares the estimated position of a nodule against the ground truth, when this is available. A corresponding localisation error is then back-propagated along with the softmax classification error. The second approach consists of a recurrent attention model that learns to observe a short sequence of smaller image portions through reinforcement learning. When a nodule annotation is available at training time, the reward function is modified accordingly so that exploring portions of the radiographs away from a nodule incurs a larger penalty. Our empirical results demonstrate the potential advantages of these architectures in comparison to competing methodologies