Automated segmentation and diagnosis of parotid tumors using a combined deep learning and radiomics model on T2-weighted MRI: a multi-vendor validation study

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Publication Details

Journal
Cancer Imaging
Published
2025-12-23
DOI
https://doi.org/10.1186/s40644-025-00982-x
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

Automated segmentation and diagnosis of parotid tumors using a combined deep learning and radiomics model on T2-weighted MRI: a multi-vendor validation study

Jiliang Ren, Ye Yuan, Qifan Ma, xiaofeng tao et al.
Cancer Imaging
Radiomics and Machine Learning in Medical Imaging
article

Automated segmentation and diagnosis of parotid tumors using a combined deep learning and radiomics model on T2-weighted MRI: a multi-vendor validation study

Jiliang Ren, Ye Yuan, Qifan Ma, xiaofeng tao, Yaqiong Ge
article en

Abstract

No abstract available for this paper.
Cancer ImagingVol. 26(1)
Shanghai Jiao Tong University (CN), Shanghai Ninth People's Hospital (CN), Beijing Chaoyang Emergency Medical Center (CN)
Openalex Percentile: Top 31%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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Automated segmentation and diagnosis of parotid tumors using a combined deep learning and radiomics model on T2-weighted MRI: a multi-vendor validation study — Jiliang Ren, Ye Yuan, et al. · Cancer Imaging (2025) | TGRS Research Map | TGRS