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

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

Wu, Kaizhi, Chen, Xi, Ding, Mingyue·2014·Optik

DOI 10.1016/j.ijleo.2014.01.114

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Authors: Wu, K., Chen, X., & Ding, M.
Affiliations: Huazhong University of Science and Technology (CN)
Corresponding author: Ding, Mingyue
ORCID: Chen, Xi
Ding, Mingyue
Venue: Optik
Publisher: Elsevier BV
Published: 2014-08
Volume: 125
Issue: 15
Pages: 4057-4063
ISSN: 0030-4026 (ISSN-L)
DOI: 10.1016/j.ijleo.2014.01.114
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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

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