The DOI matches its Crossref record.
Correction to: Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications
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Segment anything in medical images
Ma, J., He, Y., Li, F., Han, L., You, C., & Wang, B. (2024) · Nature Communications · doi:10.1038/s41467-024-44824-z · Cited by 2,913
Metadata
- Authors
- Ji, W., Li, J., Bi, Q., Liu, T., Li, W., & Cheng, L.
- Affiliations
- University of Alberta (CA)
- Wuhan University (CN)
- Dalian University of Technology (CN)
- Corresponding author
- Li, Jingjing
- Venue
- Machine Intelligence Research
- Published
- 2024-09-11
- Volume
- 21
- Issue
- 6
- Pages
- 1215-1215
- ISSN
- 1476-8186 (ISSN-L)
- 2731-538X
- 2731-5398
- OpenAlex
- W4402494392
- Cited by
- 22
- References
- 0
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Machine Intelligence Research (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- Segment Anything Model, image segmentation, real-world applications, segmentation performance evaluation
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