CHAOS

In this challenge, you segment the liver in CT data, and segment liver, spleen, and kidneys in MRI data.

The PI-CAI Challenge

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022

PANORAMA

Artificial Intelligence and Radiologists at Pancreatic Cancer Diagnosis in CT

Abdominal Circumference Operator-agnostic UltraSound measurement

The LEOPARD Challenge

AutoPET III

ToothFairy2: Multi-Structure Segmentation in CBCT Volumes

This is the second edition of the ToothFairy challenge organized by the University of Modena and Reggio Emilia with the collaboration of Radboud University Medical Center. The challenge is hosted by grand-challenge and is part of MICCAI2024.

Pelvic Bone Fragments with Injuries Segmentation Challenge

Pelvic fracture segmentation in CT and X-ray

TopCoW 2024 Challenge

Segment, classify, and detect the Circle of Willis (CoW) for both CTA and MRA

Self-supervised learning for 3D light-sheet microscopy image seg

2nd BONBID-HIE Challenge for HIE Outcome Prediction and Lesion S

UNICORN

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

CHIMERA

PANTHER Challenge

Lung nodule detection for routine clinical CT scans

Deep learning for the detection of pulmonary nodules in chest CT scans

HeviAI (A. Karagoz, et al.; Turkey) algorithm trained on PI-CAI: Private and Public Training Dataset

PI-CAI: Baseline nnU-Net (semi-supervised) trained on PI-CAI: Private and Public Training Dataset

PI-CAI: Baseline nnDetection (semi-supervised) trained on PI-CAI: Private and Public Training Dataset

BDAV_Y (Y. Yuan, et al.; Australia) algorithm trained on PI-CAI: Private and Public Training Dataset

Swangeese (H. Kan, et al.; China) algorithm trained on PI-CAI: Private and Public Training Dataset

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

PI-CAI: Baseline U-Net (supervised) trained on PI-CAI: Private and Public Training Dataset

PI-CAI: Baseline U-Net (semi-supervised) trained on PI-CAI: Private and Public Training Dataset

PI-CAI: Baseline nnDetection (supervised) trained on PI-CAI Private and Public Training Dataset

PI-CAI: Baseline nnDetection (supervised) trained on PI-CAI Private and Public Training Dataset