The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Annotation-efficient deep learning for automatic medical image segmentation
Open accessAbstract
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.
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Cites
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Clinically applicable deep learning for diagnosis and referral in retinal disease
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H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
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Self-citationRecent Advances in Artificial Intelligence Sensors
Zhang, Z., Wang, L., & Lee, C. (2023) · Advanced Sensor Research · doi:10.1002/adsr.202200072 · Cited by 107
Model-based reinforcement learning for ultrasound-driven autonomous microrobots
Medany, M., Piglia, L., Achenbach, L., Mukkavilli, S. K., & Ahmed, D. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01054-2 · Cited by 56
Wang, S., Zhou, X., Li, C., Wang, S., Li, Y., Tan, T., & Zheng, H. (2025) · Research · doi:10.34133/research.1029 · Cited by 29
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Metadata
- Authors
- Wang, S., Li, C., Wang, R., Liu, Z., Wang, M., Tan, H., Wu, Y., Liu, X., Sun, H., Yang, R., Liu, X., Chen, J., Zhou, H., Ben Ayed, I., & Zheng, H.
- Venue
- Nature Communications
- Published
- 2021-10-08
- Volume
- 12
- Issue
- 1
- Pages
- 5915-5915
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3112688606
- Cited by
- 312
- References
- 63
- FWCI
- 12.70
- Type
- journal article
- Language
- en
- Open access
- gold
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