- Grand Challenge

Yun Gu

geron

China
Shanghai Jiaotong University
Institute of Medical Robotics

Statistics

Member for 9 years, 2 months
2 challenge submissions

Activity Overview

CAMELYON17

Challenge User

Automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections. This task has high clinical relevance and would normally require extensive microscopic assessment by pathologists.

IDRiD

Challenge User

This challenge evaluates automated techniques for analysis of fundus photographs. We target segmentation of retinal lesions like exudates, microaneurysms, and hemorrhages and detection of the optic disc and fovea. Also, we seek grading of fundus images according to the severity level of DR and DME.

DRIVE

Challenge User

Develop a system to automatically segment vessels in human retina fundus images.

REFUGE

Challenge User

The goal of the Retinal Fundus Glaucoma Challenge (REFUGE) is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images.

ANHIR

Challenge User

The challenge focuses on comparing the accuracy (using manually annotated landmarks) and the approximate speed of automatic non-linear registration methods for aligning microscopy images of multi-stained histology tissue samples.

Decathlon

Challenge User

The Medical Segmentation Decathlon challenge tests the generalisability of machine learning algorithms when applied to 10 different semantic segmentation task.

Stereo Correspondence and Reconstruction of Endoscopic Data

Challenge User

Stereo Correspondence and Reconstruction of Endoscopic Data

VerSe`19

Challenge User

Vertebrae labelling and segmentation on a spine dataset on an unprecedented 150 CT scans with voxel-level vertebral annotations.

VerSe'20

Challenge User

Vertebrae labelling and segmentation on a multi-centre, multi-scanner, and anatomically-diverse CT dataset.

EndoCV2021

Challenge User

Endoscopy Computer Vision Challenge 2021

PAIP2021

Challenge User

PAIP 2021 Challenge; Perineural invasion in multiple organ cancer (colon, prostate and pancreatobiliary tract)

Multi-site, Multi-Domain Airway Tree Modeling (ATM’22)

Challenge Editor

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.

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.

Pulmonary Lobe Segmentation

Algorithm User

Automatic segmentation of pulmonary lobes on CT scans for patients with COPD or COVID-19.