PROMISE12

The goal of this challenge is to compare interactive and (semi)-automatic segmentation algorithms for MRI of the prostate.

DRIVE

Develop a system to automatically segment vessels in human retina fundus images.

PROSTATEx

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

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

K2S: from undersampled K-space To automatic Segmentation

K2S: from undersampled K-space To automatic Segmentation

PANORAMA

Artificial Intelligence and Radiologists at Pancreatic Cancer Diagnosis in CT

Scaphoid fracture detection

Automatic detection of scaphoid fractures on hand, wrist, and scaphoid x-rays.

Pulmonary Nodule Malignancy Prediction

Deep Learning for Malignancy Risk Estimation of Low-Dose Screening CT Detected Pulmonary Nodules

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.

Clinically Significant Prostate Cancer Detection in bpMRI using models trained with Report Guided Annotations

Prostate Segmentation

Whole-Gland Prostate Segmentation in bpMRI

Pancreatic Ductal Adenocarcinoma Detection in CT

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 nnDetection (supervised)

Baseline algorithm submission for PI-CAI based on the nnDetection 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)

PI-CAI: Baseline nnDetection (semi-supervised)

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

ITUnet2d

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

Hevi AI Prostate Zonal Segmentation

Zonal prostate segmentation by HeviAI

Prostate zonal segmentation by BDAV_Y

Prostate zonal segmentation by BDAV_Y

Prostate segmentation by Guerbet

Prostate segmentation by Guerbet

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

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

Fully automatic PDAC detection on CECT - PANORAMA challenge baseline