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Survey on deep learning for pulmonary medical imaging
Jiechao Ma1, Yang Song2, Xi Tian1
1InferVision, Beijing, 100020, China.
Frontiers of Medicine
|December 17, 2019
Summary
Deep learning significantly advances medical image analysis for disease diagnosis. This review covers deep learning techniques for lung diseases, improving classification, detection, and segmentation of pulmonary conditions.
Area of Science:
- Artificial Intelligence
- Medical Imaging Analysis
- Radiology
Background:
- Deep learning (DL) is a powerful AI technique.
- DL excels in analyzing complex data like images.
- Medical imaging is crucial for disease screening and diagnosis.
Purpose of the Study:
- To review major deep learning techniques in medical image analysis.
- To summarize key contributions and state-of-the-art outcomes.
- To provide an overview of DL applications in lung disease diagnosis.
Main Methods:
- Review of deep learning algorithms applied to medical imaging.
- Analysis of classification, detection, and segmentation tasks.
- Focus on pulmonary medical images and datasets.
Main Results:
- Deep learning enables automatic feature learning from medical data.
- Significant breakthroughs achieved in medical image analysis.
- DL methods show promise for various lung diseases.
Conclusions:
- Deep learning techniques are transformative for medical imaging.
- Further research is needed to address future challenges.
- DL holds great potential for improving lung disease diagnosis and management.
