Annual Review of Biomedical Engineering· 2017Q1· Review
Deep Learning in Medical Image Analysis
- 4,933citations
- Q1SCImago
- 2017year
Short summary
Deep learning methods, which learn hierarchical features directly from data, are rapidly advancing medical image analysis, outperforming hand-designed features in tasks like image registration, structure detection, segmentation, and disease diagnosis.
AI-generated from the title and abstract; the full text is not read.
Key points
- Deep learning excels by learning hierarchical features directly from data, bypassing the need for manual feature engineering.
- These methods are achieving enhanced performance across diverse medical imaging tasks.
- Applications include image registration, structure detection, tissue segmentation, and disease diagnosis/prognosis.
AI-generated from the title and abstract; the full text is not read.
Abstract
This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.
The authors' abstract, as published at the source. Annual Review of Biomedical Engineering, 2017 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science