Deep Learning in Medical Image Analysis
University of North Carolina at Chapel Hill · Korea University
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,…
Citation impact
- FWCI
- 279.48
- Percentile
- 100%
- References
- 123
Authors
3Topics & keywords
- Deep learning
- Artificial intelligence
- Computer science
- Exploit
- Medical imaging
- Segmentation
- Field (mathematics)
- Feature (linguistics)
- Quality Education
Funding
- NRNational Research Foundation
- MOMinistry of Science, ICT and Future Planning
- NRNational Research Foundation of KoreaAward: NRF-2015R1C1A1A01052216
- NINational Institutes of HealthAwards: AG042599, AG041721, EB009634, MH100217, EB008374, MH108914, EB006733, AG049371, DE022676
- IFInstitute for Information and Communications Technology Promotion