Computer-aided diagnosis in chest radiography

Chest radiographs account for more than half of all radiological examinations; the chest is the mirror of health and disease. This thesis is about techniques for computer analysis of chest radiographs. It describes methods for texture analysis and segmenting the lung fields and rib cage in a chest film. It includes a description of an automatic system for detecting regions with abnormal texture, that is applied to a database of images from a tuberculosis screening program

Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods

Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder

Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper we investigate the latest fully-convolutional architectures for the task of multi-class segmentation of the lungs field, heart and clavicles in a chest radiograph. In addition, we explore the influence of using different loss functions in the training process of a neural network for semantic segmentation. We evaluate all models on a common benchmark of 247 X-ray images from the JSRT database and ground-truth segmentation masks from the SCR dataset. Our best performing architecture, is a modified U-Net that benefits from pre-trained encoder weights. This model outperformed the current state-of-the-art methods tested on the same benchmark, with Jaccard overlap scores of 96.1% for lung fields, 90.6% for heart and 85.5% for clavicles.Comment: Presented at the First International Workshop on Thoracic Image Analysis (TIA), MICCAI 201

Health Systems in Transition: template for authors 2019

HiT health system reviews (HiTs) are based on a template that, revised periodically, provides detailed guidelines and specific questions, definitions, suggestions for data sources, and examples needed to compile HiTs. While the template offers a comprehensive set of questions, it is intended to be used in a flexible way to allow authors and editors to adapt it to their particular national context. The current version of the template is the result of a consultation process with HiT editors, previous HiT authors, Observatory National Lead Institutions (NLIs), WHO Regional Office for Europe, the European Commission, and other Observatory partners. Several sections have been reorganized to improve accessibility and clarity for readers, while the design has been greatly improved to help authors and editors in the writing process. The result is a template that is more user-friendly for authors as it now includes clear sign posting for "essential" versus "discretionary" sections as well as indicators for tables and figures. Other new features include: summary paragraphs for all chapters; a revised and extended chapter on performance assessment; and increased focus on public health and intersectorality

Challenges facing the United States of America in implementing universal coverage

In 2010, immediately before the United States of America (USA) implemented key features of the Affordable Care Act (ACA), 18% of its residents younger than 65 years lacked health insurance. In the USA, gaps in health coverage and unhealthy lifestyles contribute to outcomes that often compare unfavourably with those observed in other high-income countries. By March 2014, the ACA had substantially changed health coverage in the USA but most of its main features - health insurance exchanges, Medicaid expansion, development of accountable care organizations and further oversight of insurance companies - remain works in progress. The ACA did not introduce the stringent spending controls found in many European health systems. It also explicitly prohibits the creation of institutes - for the assessment of the cost-effectiveness of pharmaceuticals, health services and technologies - comparable to the National Institute for Health and Care Excellence in the United Kingdom of Great Britain and Northern Ireland, the Haute Autorite de Sante in France or the Pharmaceutical Benefits Advisory Committee in Australia. The ACA was - and remains - weakened by a lack of cross-party political consensus. The ACA\u27s performance and its resulting acceptability to the general public will be critical to the Act\u27s future

Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?; and, 2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.Comment: 8 pages, 3 figure