SLIVER07

The goal of this competition is to compare different algorithms to segment the liver from clinical 3D computed tomography (CT) scans.

PROSTATEx

Classification of clinical significance of prostate lesions using multi-parametric MRI data

Learn2Reg

Challenge on medical image registration addressing: learning from small datasets; estimating large deformations; dealing with multi-modal scans; and learning from noisy annotations

QUBIQ2021

Quantification of Uncertainties in Biomedical Image Segmentation Challenge 2021

TIGER

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

STOIC2021 - COVID-19 AI Challenge

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

The PI-CAI Challenge

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

CoNIC 2022

Colon Nuclei Identification and Counting Challenge 2022

LNQ2023

Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic management, and in longitudinal scans, assessing response to therapy. Current standard practice for quantifying lymph node size is based on a variety of criteria that use unidirectional or bidirectional measurements on just one or a few nodes, typically on just one axial slice. But humans have hundreds of lymph nodes, any number of which may be enlarged to various degrees due to disease or immune response. While a normal lymph node may be approximately 5mm in diameter, a diseased lymph node may be several cm in diameter. The mediastinum, the anatomical area between the lungs and around the heart, may contain ten or more lymph nodes, often with three or more enlarged greater than 1cm. Accurate segmentation in 3D would provide more information to evaluate lymph node disease.

PANORAMA

Artificial Intelligence and Radiologists at Pancreatic Cancer Diagnosis in CT

Universal Lesion Segmentation Challenge '23

Clinically Significant Prostate Cancer Detection in bpMRI

Deep learning-based 3D detection/diagnosis model trained, validated and tested using 2732 prostate biparametric MRI exams from two centers.

Prostate Segmentation

Whole-Gland Prostate Segmentation in bpMRI

PI-CAI: Baseline U-Net (supervised)

PI-CAI: Baseline nnU-Net (supervised)

Baseline algorithm submission for PI-CAI based on the nnU-Net framework

PI-CAI: Baseline nnU-Net (semi-supervised)

Baseline semi-supervised algorithm submission for PI-CAI based on the nnU-Net framework

PI-CAI: Baseline U-Net (semi-supervised)

ITUnet2d

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

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

DataScientX (N. Debs, A. Routier, et al.; France) 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