ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

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...

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Veröffentlicht in:2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) S. 3462 - 3471
Hauptverfasser: Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Bagheri, Mohammadhadi, Summers, Ronald M.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2017
Schlagworte:
Biomedical imaging
Diseases
Image segmentation
Machine learning
Pathology
X-ray imaging
ISSN:1063-6919, 1063-6919
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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 paper, we present a new chest X-ray database, namely ChestX-ray8, which comprises 108,948 frontal-view X-ray images of 32,717 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.
AbstractList 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 paper, we present a new chest X-ray database, namely ChestX-ray8, which comprises 108,948 frontal-view X-ray images of 32,717 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.
Author Bagheri, Mohammadhadi
Summers, Ronald M.
Xiaosong Wang
Zhiyong Lu
Le Lu
Yifan Peng
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  surname: Xiaosong Wang
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  organization: Dept. of Radiol. & Imaging Sci., Nat. Inst. of Health, Bethesda, MD, USA
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  surname: Yifan Peng
  fullname: Yifan Peng
  email: yifan.peng@nih.gov
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  surname: Le Lu
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  organization: Dept. of Radiol. & Imaging Sci., Nat. Inst. of Health, Bethesda, MD, USA
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  surname: Zhiyong Lu
  fullname: Zhiyong Lu
  email: luzh@nih.gov
  organization: Nat. Center for Biotechnol. Inf., Nat. Inst. of Health, Bethesda, MD, USA
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  givenname: Mohammadhadi
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  givenname: Ronald M.
  surname: Summers
  fullname: Summers, Ronald M.
  email: rms@nih.gov
  organization: Dept. of Radiol. & Imaging Sci., Nat. Inst. of Health, Bethesda, MD, USA
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Snippet 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...
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StartPage 3462
SubjectTerms Biomedical imaging
Diseases
Image segmentation
Machine learning
Pathology
X-ray imaging
Title ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
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