ICIAR 2018

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

PAIP 2019

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

EndoCV2020

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

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.

EndoCV2021

Endoscopy Computer Vision Challenge 2021

EndoCV2022

Developing methods for "detection task'' and ''segmentation task'' for endoscopic video sequence data

BCNB

Early Breast Cancer Core-Needle Biopsy WSI Dataset

AGGC22

autoPET

Automatic lesion segmentation in whole-body FDG-PET/CT

MICCAI HECKTOR 2022

Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

autoPET-II

Automated Lesion Segmentation in PET/CT - Domain Generalization

Universal Lesion Segmentation Challenge '23