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Deep Learning for Chest Radiology: A Review

  • CHEST IMAGING (H TEH, SECTION EDITOR)
  • Published:
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Abstract

Background

Compared to classical computer-aided analysis, deep learning and in particular deep convolutional neural network demonstrates breakthrough performance in many of the sophisticated chest-imaging analysis tasks, and also enables solving new problems that are infeasible to traditional machine learning.

Recent Findings

Deep learning application for radiology has shown that its performance for triaging adult chest radiography has reached a clinically acceptable level, while lung nodule detection from computed tomography has achieved interobserver variability comparable to experienced human observers, and automatically generating text report for chest radiograph is feasible.

Summary

This article will provide a review of leading and emerging deep-learning-based applications in chest radiology.

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Recently published papers of particular interest have been highlighted as: • Of importance •• Of major importance

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Authors and Affiliations

  1. AltumV Technology, 10 Anson Road #27-15, Singapore, 079903, Singapore

    Yeli Feng

  2. Ng Teng Fong General Hospital, 1 Jurong East Street 21, Singapore, 609606, Singapore

    Hui Seong Teh

  3. Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, Singapore

    Yeli Feng & Yiyu Cai

Authors
  1. Yeli Feng
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  2. Hui Seong Teh
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  3. Yiyu Cai
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Corresponding author

Correspondence to Yeli Feng.

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Yeli Feng, Hui Seong Teh, and Yiyu Cai declare no potential conflicts of interest to disclose.

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This article is part of the Topical collection on Chest Imaging.

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Feng, Y., Teh, H.S. & Cai, Y. Deep Learning for Chest Radiology: A Review. Curr Radiol Rep 7, 24 (2019). https://doi.org/10.1007/s40134-019-0333-9

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