Multiple ROI selection based focal liver lesion classification in ultrasound images
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Single feature often cannot detect all fine details of a US image, and fusion of features outperforms single feature by providing more latent and intrinsic information.
“Single feature often cannot detect all the fine details of the US image, and fusion of features outperforms single feature by providing more latent and intrinsic information12346.”
From Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector (2015) · cites this paper as [3]
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- Authors
- Jeon, J. H., Choi, J. Y., Lee, S., & Ro, Y. M.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Corresponding author
- Ro, Yong Man
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 40
- Issue
- 2
- Pages
- 450-457
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2020207580
- Cited by
- 47
- References
- 26
- Citations by year
- 2024: 2 · 2023: 6 · 2022: 3 · 2020: 3 · 2018: 3 · 2017: 4 · 2016: 14 · 2015: 5 · 2014: 6 · 2013: 1
- FWCI
- 3.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Expert Systems with Applications (publishedVersion)
- KAIST Institutional Repository (KAIST) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- focal liver lesions, ultrasound image classification, region of interest selection, liver cysts, hepatic hemangioma, malignant liver tumors, echogenicity
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