PAIP2021

PAIP 2021 Challenge; Perineural invasion in multiple organ cancer (colon, prostate and pancreatobiliary tract)

MIDOG Challenge 2021

Mitosis Domain Generalization Challenge 2021 (part of MICCAI 2021)

TIGER

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

Cell Segmentation in Multi-modality Microscopy Images

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

OCELOT 2023: Cell Detection from Cell-Tissue Interaction

MONKEY challenge: Detection of inflammation in kidney biopsies

MONKEY (Machine-learning for Optimal detection of iNflammatory cells in KidnEY)

PUMA: Panoptic segmentation of nUclei and tissue in MelanomA

The PUMA Challenge aims to enhance nuclei and tissue segmentation in melanoma histopathology, addressing the need for better prognostic biomarkers to predict treatment responses. Melanoma, a highly aggressive skin cancer, often requires immune checkpoint inhibition therapy, but only half of patients respond. Prognostic biomarkers like tumor infiltrating lymphocytes (TILs) correlate with better therapy responses and lower recurrence rate, but manual TIL scoring is subjective and inconsistent. Current deep learning methods underperform. The PUMA dataset includes annotated primary and metastatic melanoma regions to improve segmentation techniques. The challenge includes two tracks with tasks focused on tissue and nuclei segmentation, encouraging advanced methods to improve predictive accuracy.

Mitosis Detection, Fast and Slow (MDFS)

Point detection. Detection thr=0.4, with TTA. Classification thr=0.5

Tumor proportion score in non-small cell lung cancer

Automatic quantification of the tumor proportion score in NSCLC histology

Nuclei detection in immunohistochemistry

Automatic detection of nuclei (hematoxylin positive) in immunohistochemistry WSIs