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Jihan Zhang

zjh221149

  •  China
  •  Fuzhou University
  •  College of Computer and Data Science
Statistics
  • Member for 3 years
  • 21 challenge submissions
  • 16 algorithms run

Activity Overview

PI-CAI Logo
The PI-CAI Challenge
Challenge User

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

FLARE22 Logo
MICCAI FLARE 2022
Challenge User

MICCAI 2022 Fast and Low-resource semi-supervised Abdominal oRgan sEgmentation (FLARE) Challenge

ATM22 Logo
Multi-site, Multi-Domain Airway Tree Modeling (ATM’22)
Challenge User

Airway segmentation is a crucial step for the analysis of pulmonary diseases including asthma, bronchiectasis, and emphysema. The accurate segmentation based on X-Ray computed tomography (CT) enables the quantitative measurements of airway dimensions and wall thickness, which can reveal the abnormality of patients with chronic obstructive pulmonary disease (COPD). Besides, the extraction of patient-specific airway models from CT images is required for navigatiisted surgery.

HaN-Seg2023 Logo
The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge
Challenge User

A semantic multimodal segmentation challenge comprising 30 organs at risk

XPRESS Logo
Xray Projectomic Reconstruction Extracting Segment with Skeleton
Challenge User

MultiCenterAorta Logo
SEG.A. - Segmentation of the Aorta
Challenge User

Segmentation, modeling and visualization of the arterial tree are still a challenge in medical image analysis. The main track of this challenge deals with the fully automatic segmentation of the aortic vessel tree in computed tomography images. Optionally, teams can submit tailored solutions for meshing and visualization of the vessel tree.

MedFM2023 Logo
Foundation Model Prompting for Medical Image Classification
Challenge User

The primary objective of this challenge is to promote the development and evaluation of model adaptation techniques for medical image classification to leverage the existing foundation models.

SegRap2023 Logo
SegRap 2023
Challenge User

A segmentation challenge with 200 patients (two modalities of CT images, 45 OARs and 2 GTVs).