Cited by 139Radiomics and Machine Learning in Medical ImagingAI in cancer detectionMedical Image Segmentation Techniques

The DOI matches its Crossref record; 56 of the 58 references it cites were found in a registry or an index.

Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound

Overview

Integrity signals

Registry

Crossref · 10.1016/j.ijleo.2014.01.114

Cites

56 of 56 references verified0 citation errors · 0 not checkable

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Verification

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Citations

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Items 101–138 of 138

Metadata

Authors
Wu, K., Chen, X., & Ding, M.
Affiliations
Huazhong University of Science and Technology (CN)
Corresponding author
Ding, Mingyue
Venue
Optik
Publisher
Elsevier BV
Published
2014-08
Volume
125
Issue
15
Pages
4057-4063
ISSN
0030-4026 (ISSN-L)
OpenAlex
W2084415465
Cited by
139
References
58
Citations by year
2026: 4 · 2025: 9 · 2024: 15 · 2023: 21 · 2022: 18 · 2021: 25 · 2020: 15 · 2019: 13 · 2018: 6 · 2017: 6 · 2016: 1 · 2015: 6
FWCI
3.39
Type
journal article
Language
en
Open access
closed
Locations
Optik (publishedVersion)
Funders
  • National 973 Project (2011CB933103)
  • National 12th-Five Year Research Program of China (2012BAI13B02)
Topics
  • 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
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
focal liver lesions, contrast-enhanced ultrasound, benign versus malignant liver lesions, time-intensity curve, sparse non-negative matrix factorization, liver perfusion, deep learning classification

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.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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