Demo educational reader study

Reader study for demo purposes

HC18

Automated measurement of fetal head circumference using 2D ultrasound images

STOIC2021 - COVID-19 AI Challenge

COVID-19 Artificial Intelligence Challenge: automated diagnosis, and prognostic evaluation based on computed tomography

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.

Challenge Hosting Masterclass

A hands-on workshop designed to provide participants with practical experience in setting up a challenge.

Airway Anatomical Labeling

Given an airway segmentation where individual airway branches are extracted, this algorithm will automatically find 18 segmental branches, including 8 from the left lung (LB1+2, LB3, LB4, LB5, LB6, LB7+8, LB9, and LB10) and 10 from the right lung (RB1-10).

PIMed-Stanford (X. Li, S. Vesal, S. Saunders, et al.; USA) algorithm trained on PI-CAI: Private and Public Training Dataset