Deep Learning for Chest Radiology: A Review
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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.
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.
KeywordsDeep learning Pulmonary Radiography Computed tomography
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Conflicts of interest
Yeli Feng, Hui Seong Teh, and Yiyu Cai declare no potential conflicts of interest to disclose.
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