Can you develop a method for automatic detection of cancerous regions in breast cancer histology images?
NuCLS
Challenge
User
Classification, Localization and Segmentation of nuclei in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad et al. 2021. arXiv:2102.09099 [cs.CV].
TIGER
Challenge
User
Grand challenge on automate assessment of tumor infiltrating lymphocytes in digital pathology slides of triple negative and Her2-positive breast cancers
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
BCNB
Challenge
User
Early Breast Cancer Core-Needle Biopsy WSI Dataset
AGGC22
Challenge
User
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.
