Title | ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases |
Publication Type | Book Chapter |
Year of Publication | 2019 |
Authors | Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM |
Book Title | Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics |
Chapter | 18 |
Pagination | 369-392 |
Publisher | Springer, Cham |
Abstract | The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging studies accompanied by radiological reports are accumulated and stored in many modern hospitals’ picture archiving and communication systems (PACS) . On the other side, it is still an open question how this type of hospital-size knowledge database containing invaluable imaging informatics (i.e., loosely labeled) can be used to facilitate the data-hungry deep learning paradigms in building truly large-scale high-precision computer-aided diagnosis (CAD) systems. In this chapter, we present a chest X-ray database, namely, “ChestX-ray”, which comprises 121,120 frontal-view X-ray images of 30,805 unique patients with the text-mined eight disease image labels (where each image can have multi-labels), from the associated radiological reports using natural language processing. Importantly, we demonstrate that these commonly occurring thoracic diseases can be detected and even spatially located via a unified weakly supervised multi-label image classification and disease localization framework, which is validated using our proposed dataset. Although the initial quantitative results are promising as reported, deep convolutional neural network-based “reading chest X-rays” (i.e., recognizing and locating the common disease patterns trained with only image-level labels) remains a strenuous task for fully automated high-precision CAD systems. |
DOI | 10.1007/978-3-030-13969-8_18 |