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

Uncertainty-aware multiple-instance learning for reliable classification:Application to optical coherence tomography

Deep learning classification models for medical image analysis often perform well on data from scanners that were used to acquire the training data. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability. We aimed to enhance the reliability of deep learning classification models using a novel method called Uncertainty-Based Instance eXclusion (UBIX). UBIX is an inference-time module that can be employed in multiple-instance learning (MIL) settings. MIL is a paradigm in which instances (generally crops or slices) of a bag (generally an image) contribute towards a bag-level output. Instead of assuming equal contribution of all instances to the bag-level output, UBIX detects instances corrupted due to local artifacts on-the-fly using uncertainty estimation, reducing or fully ignoring their contributions before MIL pooling. In our experiments, instances are 2D slices and bags are volumetric images, but alternative definitions are also possible. Although UBIX is generally applicable to diverse classification tasks, we focused on the staging of age-related macular degeneration in optical coherence tomography. Our models were trained on data from a single scanner and tested on external datasets from different vendors, which included vendor-specific artifacts. UBIX showed reliable behavior, with a slight decrease in performance (a decrease of the quadratic weighted kappa (κw) from 0.861 to 0.708), when applied to images from different vendors containing artifacts; while a state-of-the-art 3D neural network without UBIX suffered from a significant detriment of performance (κw from 0.852 to 0.084) on the same test set. We showed that instances with unseen artifacts can be identified with OOD detection. UBIX can reduce their contribution to the bag-level predictions, improving reliability without retraining on new data. This potentially increases the applicability of artificial intelligence models to data from other scanners than the ones for which they were developed. The source code for UBIX, including trained model weights, is publicly available through https://github.com/qurAI-amsterdam/ubix-for-reliable-classification.</p

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