Back to Search
Start Over
Annotation-efficient deep learning for automatic medical image segmentation
- Source :
- Nature Communications, Vol 12, Iss 1, Pp 1-13 (2021), Nature Communications
- Publication Year :
- 2021
- Publisher :
- Nature Portfolio, 2021.
-
Abstract
- Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, we introduce Annotation-effIcient Deep lEarning (AIDE), an open-source framework to handle imperfect training datasets. Methodological analyses and empirical evaluations are conducted, and we demonstrate that AIDE surpasses conventional fully-supervised models by presenting better performance on open datasets possessing scarce or noisy annotations. We further test AIDE in a real-life case study for breast tumor segmentation. Three datasets containing 11,852 breast images from three medical centers are employed, and AIDE, utilizing 10% training annotations, consistently produces segmentation maps comparable to those generated by fully-supervised counterparts or provided by independent radiologists. The 10-fold enhanced efficiency in utilizing expert labels has the potential to promote a wide range of biomedical applications.<br />Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, the authors introduce an open-source framework to handle imperfect training datasets.
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Science
Computer Science - Computer Vision and Pattern Recognition
Datasets as Topic
General Physics and Astronomy
Breast Neoplasms
Image processing
Machine learning
computer.software_genre
Medical care
Article
General Biochemistry, Genetics and Molecular Biology
Machine Learning (cs.LG)
Annotation
Deep Learning
Image Processing, Computer-Assisted
FOS: Electrical engineering, electronic engineering, information engineering
Humans
Segmentation
Retrospective Studies
Multidisciplinary
business.industry
Deep learning
Image and Video Processing (eess.IV)
General Chemistry
Image segmentation
Electrical Engineering and Systems Science - Image and Video Processing
Magnetic Resonance Imaging
Range (mathematics)
ComputingMethodologies_PATTERNRECOGNITION
Female
Artificial intelligence
business
Biomedical engineering
computer
Subjects
Details
- Language :
- English
- ISSN :
- 20411723
- Volume :
- 12
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Nature Communications
- Accession number :
- edsair.doi.dedup.....5d3c0fb2d768c9ca8ef5ef345c63e38e