De-Jun FAN

De-Jun FAN

Endoscopist

Sun Yat-sen University



Dr. De-Jun FAN works in the Department of Gastrointestinal Endoscopy (GIE) of the Sixth Affiliated Hospital of SYSU since 2016. He is proficient in endoscopic resection of benign lesion of the digestive tract, endoscopic treatment of gastrointestinal bleeding, endoscopic dilatation, and endoscopic dissection for early cancer and submucosal tumors of the digestive tract. His research interest included the biological function and related mechanisms in the development of colorectal cancer. His recent research interest is the interdiscipline of GIE and artificial intelligence (AI).

Interests

  • Gastrointestinal Endoscopy
  • Artificial Intelligence

Education

  • Doctor of Medicine in General Surgery, 2020 - now

    Sun Yat-sen University

  • Master of Medicine in Surgery, 2013 - 2016

    Sun Yat-sen University

  • Bachelor of Medicine in Clinical Medicine, 2008 - 2013

    Sun Yat-sen University

Publications

Cross-Level Contrastive Learning and Consistency Constraint for Semi-Supervised Medical Image Segmentation
An immune, stroma, and epithelial--mesenchymal transition-related signature for predicting recurrence and chemotherapy benefit in stage II--III colorectal cancer
Predicting prognosis and immunotherapy response among colorectal cancer patients based on a tumor immune microenvironment-related lncRNA signature
A tumor immune microenvironment-related lncRNA signature for the prognosis and immunotherapeutic sensitivity prediction in colorectal cancer.
PIANOS: A platform independent and normalization free single-sample classifier for colorectal cancer
Cross-level contrastive learning and consistency constraint for semi-supervised medical image segmentation
DNA Repair–Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients
Lesion-aware dynamic kernel for polyp segmentation
Identification of an autophagy-related gene signature for the prediction of prognosis in early-stage colorectal cancer
A model combing an immune-related genes signature and an extracellular matrix-related genes signature in predicting prognosis of left-and right-sided colon cancer
Deep learning to identify a gene signature associated with molecular subtypes that predicts prognosis in colorectal cancer.
Identifying an immune-related gene-pair for prognosis prediction of metastatic colorectal cancer.