- Grand Challenge

yan she

橘色闪光

China
hehe
haha

Statistics

Member for 3 years, 3 months

Activity Overview

PROMISE12

Challenge User

The goal of this challenge is to compare interactive and (semi)-automatic segmentation algorithms for MRI of the prostate.

LUNA16

Challenge User

The LUNA16 challenge: automatic nodule detection on chest CT

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.

KiTS19

Challenge User

2019 Kidney and Kidney Tumor Segmentation Challenge

COVID-19 LUNG CT LESION SEGMENTATION CHALLENGE - 2020

Challenge User

This challenge will create the platform to evaluate emerging methods for the segmentation and quantification of lung lesions caused by SARS-CoV-2 infection from CT images.

Breast Cancer Segmentation

Challenge User

Semantic segmentation of histologic regions in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083

NODE21

Challenge User

NODE21: generate and detect nodules on chest radiographs

The PI-CAI Challenge

Challenge User

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

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.

Breast Cancer Immunohistochemical Image Generation Challenge

Challenge User

The Breast Cancer Immunohistochemical Image Generation Challenge aims to directly generate IHC-stained breast cancer histopathology images from HE-stained images.