Cited by 47Medical Image Segmentation TechniquesRadiomics and Machine Learning in Medical ImagingAI in cancer detection

Multiple ROI selection based focal liver lesion classification in ultrasound images

Jeon, Jae Hyun, Choi, Jae Young, Lee, Sihyoung, Ro, Yong Man·2013·Expert Systems with Applications

Integrity signals

Registry

Crossref · 10.1016/j.eswa.2012.07.053

Cites

25 of 25 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

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Claims other papers make about this one

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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.”

Cites

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1 of the 26 references couldn’t be matched to a known paper.

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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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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