RAVIR

A dataset for semantic segmentation and quantitative analysis of retinal arteries and veins in infrared reflectance imaging

Ischemic Stroke Lesion Segmentation Challenge

Universal Lesion Segmentation Challenge '23

Diagnostic Report Analysis: General Optimization of NLP

AutoPET III

Ischemic Stroke Lesion Segmentation Challenge 2024

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.

TrackRad: Real-time tumor tracking for MRI-guided radiotherapy