Weidao Y. Chen
Beijing, China
Last Updated: 09/26/2026
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Found 21 publications
End-to-End Deep Learning Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Patients Using Routine MRI.
Journal: Journal of imaging informatics in medicine
Published: December 12, 2024
End-to-end deep learning for the diagnosis of pelvic and sacral tumors using non-enhanced MRI: a multi-center study.
Journal: NPJ precision oncology
Published: November 24, 2024
Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study.
Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
Published: October 06, 2024
Deep Learning-Based Artificial Intelligence Model for Automatic Carotid Plaque Identification
Journal: Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
Published: August 18, 2024
Radiomics combined with clinical and MRI features may provide preoperative evaluation of suboptimal debulking surgery for serous ovarian carcinoma.
Journal: Abdominal radiology (New York)
Published: February 27, 2024
HE-Mind: A model for automatically predicting hematoma expansion after spontaneous intracerebral hemorrhage.
Journal: European journal of radiology
Published: January 16, 2024
A deep learning algorithm to identify carotid plaques and assess their stability.
Journal: Frontiers in artificial intelligence
Published: October 15, 2023
Scalable Swin Transformer network for brain tumor segmentation from incomplete MRI modalities.
Journal: Artificial intelligence in medicine
Published: June 12, 2023
Deep learning-assisted LI-RADS grading and distinguishing hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT: a two-center study.
Journal: European radiology
Published: April 03, 2023
An MRI-based machine learning radiomics can predict short-term response to neoadjuvant chemotherapy in patients with cervical squamous cell carcinoma: A multicenter study.
Journal: Cancer medicine
Published: March 30, 2023
A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients.
Journal: Biomedical engineering online
Published: January 28, 2023
Last Updated: 09/26/2026