CORADS Score Practice

Practice CORADS scoring with 50 cases. You get instant feedback after every case.

PROMISE12

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

RETOUCH

Retinal OCT Fluid Challenge (RETOUCH) compares automated algorithms that are able to detect and segment different types of retinal fluid in optical coherence tomography (OCT).

ROCC

Retinal OCT Classification Challenge (ROCC) is organized as a one day Challenge in conjunction with MVIP2017. The goal of this challenge is to call different automated algorithms that are able to detect DR disease from normal retina on a common dataset of OCT volumes, acquired with Topcon SD-OCT devices.

DRIVE

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

REFUGE

The goal of the Retinal Fundus Glaucoma Challenge (REFUGE) is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images.

PROSTATEx

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

PALM

The Pathologic Myopia Challenge (PALM) focuses on the investigation and development of algorithms associated with the diagnosis of Pathological Myopia (PM) and segmentation of lesions in fundus photos from PM patients.

iChallenges

We organized a serial of challenges on different eye image modalities, such as REFUGE, PALM, RETOUCH, etc.

ODIR-2019

北京大学国际眼底图像智能识别竞赛 Peking University International Competition on Ocular Disease Intelligent Recognition

Learn2Reg

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

RIADD (ISBI-2021)

Retinal Image Analysis for multi-Disease Detection

NODE21

NODE21: generate and detect nodules on chest radiographs

AIROGS

Artificial Intelligence for RObust Glaucoma Screening Challenge

SynthRAD2023

SynthRAD is the first challenge on automatic generation of synthetic computed tomography (sCT) for radiotherapy

Diabetic Retinopathy Analysis Challenge MICCAI2022

Diabetic Retinopathy (DR) lesions segmentation, image quality assessment and classification of proliferatived DR (PDR) and non-PDR in ultra-wide optical coherence tomography angiography mosaic (UW-OCTA-M) images

Vertebra segmentation and labeling

Segmentation and labeling of the vertebrae in CT scans with arbitrary field of view.

Gleason Grading of Prostate Biopsies

Automated Gleason grading of prostate biopsies following the Gleason Grade Group system.

Pulmonary Lobe Segmentation

Automatic segmentation of pulmonary lobes on CT scans for patients with COPD or COVID-19.

CORADS-AI

Segments pulmonary lobes and lesions and computes the CORADS and CT Severity Score from a non-contrast CT scan.

Pulmonary Nodule Malignancy Prediction

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

Fluid Segmentation in Retinal Optical Coherence Tomography (OCT)

Segments intraretinal fluid, subretinal fluid, and pigment epithelial detachments in Optical Coherence Tomography scans. Optimized for Spectralis, Cirrus and Topcon scanners.

Whole-heart segmentation in non-contrast-enhanced CT

Algorithm for the automatic segmentation of cardiac structures in non-contrast-enhanced CT images. The structures to be segmented are the left ventricular myocardium, left ventricular cavity, right ventricle, left atrium, right atrium, ascending aorta, and the pulmonary artery trunk until the first bifurcation. The underlying publication can be found under https://doi.org/10.1002/mp.14451

AIROGS Baseline

A baseline algorithm for the AIROGS challenge

airogs_ur

Submission for AIROGS challenge

AIROGS Classifier

A end to end deep learning based system to find referable glaucoma and non-referable glaucoma.

base_airgos_update

airogs_v5

Airogs no-batch final

Glaucoma detection with OoD

Age-related macular degeneration (AMD) Staging in Optical Coherence Tomography (OCT) with UBIX for Increased Reliability

Deep learning models for optical coherence tomography (OCT) classification often perform well on data from scanners that were also used during training. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability. We aimed to improve this generalizability of deep learning models for OCT classifi- cation. To reduce the effect of vendor-specific artifacts, we propose Uncertainty-Based Instance eXclusion (UBIX), of which we define a hard and a soft variant. UBIX aims to suppress the contributions of B-scans with unseen artifacts to the final OCT-level outputs. Suppression is based on out-of-distribution detection of B-scans, which are instances in our multiple instance learning approach.