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Lesion-aware convolutional neural network for chest radiograph classification.

F Li1, J-X Shi1, L Yan1

  • 1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.

Clinical Radiology
|October 20, 2020
PubMed
Summary
This summary is machine-generated.

A novel deep-learning model, lesion-aware convolutional neural network (LACNN), demonstrates radiologist-level performance in identifying thoracic diseases on chest X-rays (CXRs). This AI approach could significantly improve diagnostic speed and patient access to critical medical imaging analysis.

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology and Diagnostic Imaging
  • Machine Learning for Healthcare

Background:

  • Chest X-rays (CXRs) are crucial for diagnosing thoracic diseases.
  • Accurate and timely interpretation of CXRs is essential for patient outcomes.
  • Deep learning models offer potential for enhancing diagnostic accuracy and efficiency.

Purpose of the Study:

  • To evaluate the performance of a lesion-aware convolutional neural network (LACNN) for identifying 14 thoracic diseases on CXRs.
  • To compare the diagnostic performance of LACNN against that of experienced radiologists.
  • To assess the generalizability and efficiency of the LACNN model.

Main Methods:

  • Retrospective collection and analysis of 10,738 CXRs from 3,526 patients.
  • Development and training of a lesion-detection network (LDN) and a classification network within LACNN.
  • Validation on an independent dataset (ChestX-ray14) and comparison with radiologist performance.
  • Utilisation of occlusion testing for model interpretability and analysis of non-image data integration.

Main Results:

  • LACNN achieved statistically significant higher AUC performance than radiologists for atelectasis, mass, and nodule detection.
  • No significant performance differences were observed for the other 11 pathologies.
  • LACNN processed CXRs significantly faster (0.197 seconds) than radiologists (∼35 seconds).
  • The model showed competitive performance against state-of-the-art deep learning methods on the ChestX-ray14 dataset.

Conclusions:

  • The proposed LACNN achieves radiologist-level performance in thoracic disease identification from CXRs.
  • LACNN demonstrates potential to improve diagnostic efficiency and expand patient access to CXR diagnostics.
  • Integration of non-image data slightly enhanced model performance.