A clinically validated 3D deep learning approach for quantifying vascular invasion in pancreatic cancer - Top-Institutions - MDSpire

A clinically validated 3D deep learning approach for quantifying vascular invasion in pancreatic cancer

  • By

  • Yajiao Zhang

  • Haoran Zhang

  • Yanzhao Yang

  • Chao Wu

  • Lei Zhang

  • Wei Xia

  • Xue Wang

  • Xiaohuan Zhang

  • Lixiu Cao

  • Manju Liu

  • Jing Zhang

  • Fuhua Yan

  • Baiyong Shen

  • Ning Wen

  • December 31, 2025

  • 0 min

Share

Top Institutions in General Medicine

Brief introduction explaining scope and methodology.

  • #1

    Memorial Sloan Kettering Cancer Center
    Memorial Sloan Kettering Cancer Center

    New York, NY

    Key Differentiators

    • Pancreatic Surgery
    • Radiology
    • Artificial Intelligence in Oncology

  • #2

    Johns Hopkins Hospital
    Johns Hopkins Hospital

    Baltimore, MD

    Key Differentiators

    • Pancreatic Oncology
    • Radiology
    • Medical Imaging Research

  • #3

    MD Anderson Cancer Center
    MD Anderson Cancer Center

    Houston, TX

    Key Differentiators

    • Surgical Oncology
    • Radiology
    • Cancer Imaging Informatics

  • #4

    Massachusetts General Hospital
    Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Radiology
    • Surgical Oncology
    • Biomedical Engineering

  • #5

    University of Pittsburgh Medical Center (UPMC)
    University of Pittsburgh Medical Center (UPMC)

    Pittsburgh, PA

    Key Differentiators

    • Pancreatic Surgery
    • Radiology
    • Artificial Intelligence

Original Source(s)

A clinically validated 3D deep learning approach for quantifying vascular invasion in pancreatic cancer

  • by Yajiao Zhang, Haoran Zhang, Yanzhao Yang, Chao Wu, Lei Zhang, Wei Xia, Xue Wang, Xiaohuan Zhang, Lixiu Cao, Manju Liu, Jing Zhang, Fuhua Yan, Baiyong Shen, Ning Wen

  • December 31, 2025

Related Content

Anatomically-guided Masked Autoencoder with Domain-Adaptive Prompting (AMAP) for multimodal cerebral aneurysm detection and segmentation

A generalizable 3D framework and model for self-supervised learning in medical imaging

STD-Net: a spatio-temporal decoupling network for multiphasic liver lesion segmentation and characterization