ResearcherShanghai Jiao Tong UniversityNo recent work listed
Baiwen Li
Matched on ORCID iD 0000-0003-0815-5835; OpenAlex author ID A5000056230. Sources: OpenAlex.
Their work
- Papers
- 2 (first author 0, last author 1)
- Trials
- 0
- Grants
- 0
- Labs led
- 0
- Collaborators
- 5
2023: 2 papers
Papers 2
- Peritumoral and Intratumoral Texture Features Based on Multiparametric MRI and Multiple Machine Learning Methods to Preoperatively Evaluate the Pathological Ou… Co-author DOI 10.1002/jmri.28538↗
- Preoperative Extrapancreatic Extension Prediction in Patients with Pancreatic Cancer Using Multiparameter MRI and Machine Learning-Based Radiomics Model. Last author DOI 10.1016/j.acra.2022.09.017↗
Closest collaborators
The 5 strongest of 5 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Ni Xie · 2 shared papers, 2023 · strength 0.36
- Paper Preoperative Extrapancreatic Extension Prediction in Patients with Pancreatic Cancer Using Multiparameter MRI and Machi… DOI 10.1016/j.acra.2022.09.017↗
- Paper Peritumoral and Intratumoral Texture Features Based on Multiparametric MRI and Multiple Machine Learning Methods to Pre… DOI 10.1002/jmri.28538↗
- Hao Liu · 2 shared papers, 2023 · strength 0.36
- Paper Preoperative Extrapancreatic Extension Prediction in Patients with Pancreatic Cancer Using Multiparameter MRI and Machi… DOI 10.1016/j.acra.2022.09.017↗
- Paper Peritumoral and Intratumoral Texture Features Based on Multiparametric MRI and Multiple Machine Learning Methods to Pre… DOI 10.1002/jmri.28538↗
- Xuhui Fan · 2 shared papers, 2023 · strength 0.36
- Paper Preoperative Extrapancreatic Extension Prediction in Patients with Pancreatic Cancer Using Multiparameter MRI and Machi… DOI 10.1016/j.acra.2022.09.017↗
- Paper Peritumoral and Intratumoral Texture Features Based on Multiparametric MRI and Multiple Machine Learning Methods to Pre… DOI 10.1002/jmri.28538↗
- Lanting Yu · 1 shared paper, 2023 · strength 0.18
- Han Wang · 1 shared paper, 2023 · strength 0.18
How to reach this person
A chain of introductions through people who have published, run trials or held grants together (warm_path@1: fewest links first, then the strongest). Built from public records only; no contact details are kept.