LUNA16

The LUNA16 challenge: automatic nodule detection on chest CT

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

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.

TIGER

Grand challenge on automate assessment of tumor infiltrating lymphocytes in digital pathology slides of triple negative and Her2-positive breast cancers

CoNIC 2022

Colon Nuclei Identification and Counting Challenge 2022

AGGC22

Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022

PAIP 2023: TC prediction in pancreatic and colon cancer

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

UNICORN

Grand challenge on benchmarking vision-language foundation models in the digital pathology and radiology domain

The LUNA25 Challenge