知名学者引用


        除了所发表论文的被引用频次、期刊质量能够彰显学者的学术影响力以外,被知名学者引用也是一项学术评价的重要指标。
        当前全球学者库认定的知名学者包括:诺贝尔奖、菲尔兹奖、图灵奖等奖项获得者,多个国家的院士,全球学者库“2023全球学者学术影响力排行版”入榜的前10万学者等。

Zhong, Yifan 同济大学被知名学者引用:47人次(黄金会员以上可以申请导出本人论文被全部知名学者引用的数据)

1. 陈孝平 中国 华中科技大学 中国科学院院士 Insight into the structure, physiological function, and role in cancer of m6A readers-YTH domain-con..

2. Rodrigo,Dienstmann 西班牙 Autonomous University of Barcelona A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still l..

3. Regina G. H,Beets-Tan 荷兰 Netherlands Cancer Institute A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still l..

4. 单鸿 中国 中山大学 Computer-aided diagnosis of distal metastasis in non-small cell lung cancer by low-dose CT based rad..

5. Genichiro,Ishii 日本 National Cancer Center Hospital East Prognostic value of predominant subtype in pathological stage II-III lung adenocarcinoma with epider..

6. 李培峰 中国 青岛大学 The important regulatory roles of circRNA-encoded proteins or peptides in cancer pathogenesis[Review..

7. Seung Hong,Choi 韩国 Seoul National University Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recur..

8. Park, Chul-Kee 韩国 Seoul National University Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recur..

9. Park, Sung-Hye 韩国 Seoul National University Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recur..

10. Filippo,de Braud 意大利 Foundation IRCCS Ist Nazl Tumori Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

11. Marina Chiara,Garassino 意大利 Foundation IRCCS Ist Nazl Tumori Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

12. George,Pentheroudakis 希腊 University of Ioannina Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

13. 白春学 中国 复旦大学上海医学院 An artificial intelligence-assisted diagnostic system for the prediction of benignity and malignancy..

14. Alberto M,Marchevsky 美国 Cedars-Sinai Medical Center Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Th..

15. Shinji,Naganawa 日本 Nagoya University Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis predict..

16. 朱峰 中国 浙江大学 An interpretable ensemble learning model facilitates early risk stratification of ischemic stroke in..

17. 王朝霞 中国 南京医科大学 Characterization of circRNAs in established osimertinib-resistant non-small cell lung cancer cell li..

18. Jin Mo,Goo 韩国 Seoul National University Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopatho..

19. Shinji,Naganawa 日本 Nagoya University New trend in artificial intelligence-based assistive technology for thoracic imaging

20. Fang-Fang,Yin 美国 Duke University Development of a multi-feature-combined model: proof-of-concept with application to local failure pr..

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