- Grand Challenge

WEI LOU

lewislou0210

China
Chinese university of HongKong
Computer and Information System

Statistics

Member for 2 years, 6 months
50 challenge submissions

Activity Overview

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.

DigestPath2019

Challenge User

Welcome to Digestive-System Pathological Detection and Segmentation Challenge 2019. This competition is part of the MICCAI 2019 Challenge.

PAIP2021

Challenge User

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

WSSS4LUAD

Challenge User

The WSSS4LUAD dataset contains over 10,000 patches of lung adenocarcinoma from whole slide images from Guangdong Provincial People's Hospital and TCGA with image-level annotations. The goal of this challenge is to perform semantic segmentation for differentiating three important types of tissues in the WSIs of lung adenocarcinoma, including cancerous epithelial region, cancerous stroma region and normal region. Paticipants have to use image-level annotations to give pixel-level prediction.

CoNIC 2022

Challenge User

Colon Nuclei Identification and Counting Challenge 2022

ACROBAT 2023

Challenge User

The ACROBAT challenge aims to advance the development of WSI registration algorithms that can align WSIs of IHC-stained breast cancer tissue sections to corresponding tissue regions that were stained with H&E. All WSIs originate from routine diagnostic workflows.

Cell Segmentation in Multi-modality Microscopy Images

Challenge User

Weakly Supervised Cell Segmentation in Multi-modality High-resolution Microscopy Images

PAIP 2023: TC prediction in pancreatic and colon cancer

Challenge User

Tumor cellularity prediction in pancreatic cancer (supervised learning) and colon cancer (transfer learning)