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Erschienen in:
Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

18. ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases

verfasst von : Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald M. Summers

Erschienen in: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Deep Learning for Functional Brain Connectivity: Are We There Yet?
Nächstes Kapitel Automatic Classification and Reporting of Multiple Common Thorax Diseases Using Chest Radiographs
Fußnoten
3
Data split files could be downloaded via https://​nihcc.​app.​box.​com/​v/​ChestXray-NIHCC.
 
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Metadaten
Titel
ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases
verfasst von
Xiaosong Wang
Yifan Peng
Le Lu
Zhiyong Lu
Mohammadhadi Bagheri
Ronald M. Summers
Copyright-Jahr
2019
Buch
Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

Print ISBN: 978-3-030-13968-1

Electronic ISBN: 978-3-030-13969-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-13969-8_18

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