Deep Learning for Chest Radiology: A Review

  • Yeli FengEmail author
  • Hui Seong Teh
  • Yiyu Cai
Part of the following topical collections:
  1. Chest Imaging



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.


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


Deep learning Pulmonary Radiography Computed tomography 


Compliance with Ethical Guidelines

Conflicts of interest

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


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

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.AltumV TechnologySingaporeSingapore
  2. 2.Ng Teng Fong General HospitalSingaporeSingapore
  3. 3.Nanyang Technological UniversitySingaporeSingapore

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