Bingzhong Jing
State Key Laboratory Of Oncology In South China
Guangzhou, 510060, China
Guangzhou, 510060, China
Last Updated: 09/26/2026
Save publications for later
Sign Up
Not sure about your diagnosis?
Check Your Symptoms
Found 16 publications
Automated Tumor and Node Staging from Esophageal Cancer Endoscopic Ultrasound Reports: A Benchmark of Advanced Reasoning Models with Prompt Engineering and Cross-Lingual Evaluation.
Journal: Diagnostics (Basel, Switzerland)
Published: October 16, 2025
TGAP-Net: Twin Graph Attention Pseudo-Label Generation for Weakly Supervised Semantic Segmentation.
Journal: IEEE journal of biomedical and health informatics
Published: March 03, 2025
Establishment of a deep-learning-assisted recurrent nasopharyngeal carcinoma detecting simultaneous tactic (DARNDEST) with high cost-effectiveness based on magnetic resonance images: a multicenter study in an endemic area.
Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
Published: September 10, 2024
Deep learning model with pathological knowledge for detection of colorectal neuroendocrine tumor.
Journal: Cell reports. Medicine
Published: June 10, 2024
Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging.
Journal: Cell reports. Medicine
Published: November 23, 2023
Deep learning-based recurrence detector on magnetic resonance scans in nasopharyngeal carcinoma: A multicenter study.
Journal: European journal of radiology
Published: May 11, 2023
BézierSeg: Parametric Shape Representation for Fast Object Segmentation in Medical Images.
Journal: Life (Basel, Switzerland)
Published: January 28, 2023
The contrast-enhanced MRI can be substituted by unenhanced MRI in identifying and automatically segmenting primary nasopharyngeal carcinoma with the aid of deep learning models: An exploratory study in large-scale population of endemic area.
Journal: Computer methods and programs in biomedicine
Published: August 09, 2021
Development of a self-constrained 3D DenseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance images.
Journal: Oral oncology
Published: April 07, 2020
Prognostic and Treatment Guiding Significance of MRI-Based Tumor Burden Features and Nodal Necrosis in Nasopharyngeal Carcinoma.
Journal: Frontiers in oncology
Published: February 23, 2020
Deep learning for risk prediction in patients with nasopharyngeal carcinoma using multi-parametric MRIs.
Journal: Computer methods and programs in biomedicine
Published: December 06, 2019
Last Updated: 09/26/2026