- Grand Challenge

Joe Gu

guran765965810

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Member for 6 years, 1 month
16 challenge submissions

Activity Overview

ICIAR 2018

Challenge User

Can you develop a method for automatic detection of cancerous regions in breast cancer histology images?

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.

CHAOS

Challenge User

In this challenge, you segment the liver in CT data, and segment liver, spleen, and kidneys in MRI data.

KiTS19

Challenge User

2019 Kidney and Kidney Tumor Segmentation Challenge

PAIP 2019

Challenge User

PAIP2019: Liver Cancer Segmentation Task 1: Liver Cancer Segmentation Task 2: Viable Tumor Burden Estimation

DigestPath2019

Challenge User

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

EndoCV2020

Challenge User

Endoscopy computer vision challenge (EndoCV2020) introduces two core sub-themes in endoscopy: 1) artefact detection and segmentation (EAD2020) and 2) disease detection and segmentation (EDD2020).

PAIP2020

Challenge User

Built on the success of its predecessor, PAIP2020 is the second challenge organized by the Pathology AI Platform (PAIP) and the Seoul National University Hospital (SNUH). PAIP2020 will proceed to not only detect whole tumor areas in colorectal cancers but also to classify their molecular subtypes, which will lead to characterization of their heterogeneity with respect to prognoses and therapeutic responses. All participants should predict one of the molecular carcinogenesis pathways, i.e., microsatellite instability(MSI) in colorectal cancer, by performing digital image analysis without clinical tests. This task has a high clinical relevance as the currently used procedure requires an extensive microscopic assessment by pathologists. Therefore, those automated algorithms would reduce the workload of pathologists as a diagnostic assistance.

QUBIQ

Challenge User

Quantification of Uncertainties in Biomedical Image 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.

EndoCV2021

Challenge User

Endoscopy Computer Vision Challenge 2021

Cross-Modality Domain Adaptation Image Segmentation - 2021

Challenge User

The CrossMoDA challenge 2021 introduces the first large and multi-class medical dataset for unsupervised cross-modality Domain Adaptation.

Cell Segmentation in Multi-modality Microscopy Images

Challenge User

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