CAMELYON17

Automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections. This task has high clinical relevance and would normally require extensive microscopic assessment by pathologists.

ICIAR 2018

Can you develop a method for automatic detection of cancerous regions in breast cancer histology images?

SLIVER07

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

DRIVE

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

BreastPathQ: Cancer Cellularity Challenge 2019

SPIE-AAPM-NCI BreastPathQ:Cancer Circularity Challenge 2019: Participants will be tasked to develop an automated method for analyzing histology patches extracted from whole slide images and assign a score reflecting cancer cellularity for tumor burden assessment in each.

Decathlon

The Medical Segmentation Decathlon challenge tests the generalisability of machine learning algorithms when applied to 10 different semantic segmentation task.

KiTS19

2019 Kidney and Kidney Tumor Segmentation Challenge

PAIP 2019

PAIP2019: Liver Cancer Segmentation Task 1: Liver Cancer Segmentation Task 2: Viable Tumor Burden Estimation

StructSeg2019

Welcome to Automatic Structure Segmentation for Radiotherapy Planning Challenge 2019. This competition is part of the MICCAI 2019 Challenge.

DigestPath2019

Welcome to Digestive-System Pathological Detection and Segmentation Challenge 2019. This competition is part of the MICCAI 2019 Challenge.

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

Lymphocyte Assessment Hackathon

Lymphocyte Assessment Hackathon in conjunction with the MICCAI COMPAY 2019 Workshop on Computational Pathology

HEROHE

Unlike previous challenges, this proposes to find an image analysis algorithm to identify HER2-positive from HER2-negative breast cancer specimens evaluating only the morphological features present on the HE slide, without the staining patterns of IHC.

EndoCV2020

Endoscopy computer vision challenge (EndoCV2020) introduces two core sub-themes in endoscopy: 1) artefact detection and segmentation (EAD2020) and 2) disease detection and segmentation (EDD2020).

LNDb Challenge

Lung cancer screening and Fleischner follow-up determination in chest CT through nodule detection, segmentation and characterization

PAIP2020

Built on the success of its predecessor, PAIP2020 is the second challenge organized by the Pathology AI Platform (PAIP) and the Seoul National University Hospital (SNUH). PAIP2020 will proceed to not only detect whole tumor areas in colorectal cancers but also to classify their molecular subtypes, which will lead to characterization of their heterogeneity with respect to prognoses and therapeutic responses. All participants should predict one of the molecular carcinogenesis pathways, i.e., microsatellite instability(MSI) in colorectal cancer, by performing digital image analysis without clinical tests. This task has a high clinical relevance as the currently used procedure requires an extensive microscopic assessment by pathologists. Therefore, those automated algorithms would reduce the workload of pathologists as a diagnostic assistance.

RibFrac

Rib Fracture Detection and Classification Challenge: A large-scale benchmark of 660 CT scans with ~5,000 rib fractures (around 80Gb)

Automated Segmentation Of Coronary Arteries

Automated Segmentation Of Coronary Arteries

SARAS-ESAD

This challenge is part of Medical Imaging with Deep Learning conference, 2020. The conference is held between 6 ‑ 8 July 2020 in Montréal. The SARAS (Smart Autonomous Robotic Assistant Surgeon) EU consortium, www.saras-project.eu, is working towards replacing the assistant surgeon in MIS with two assistive robotic arms. To accomplish that, an artificial intelligence based system is required which not only can understand the complete surgical scene but also detect the actions being performed by the main surgeon. This information can later be used infer the response required from the autonomous assistant surgeon.

CT diagnosis of COVID-19

Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. To mitigate the inefficiency and shortage of existing tests for COVID-19, we propose this competition to encourage the development of effective Deep Learning techniques to diagnose COVID-19 based on CT images. The problem we want to solve is to classify each CT image into positive COVID-19 (the image has clinical findings of COVID-19) or negative COVID-19 ( the image does not have clinical findings of COVID-19). It’s a binary classification problem based on CT images.

RIADD (ISBI-2021)

Retinal Image Analysis for multi-Disease Detection

COVID-19 LUNG CT LESION SEGMENTATION CHALLENGE - 2020

This challenge will create the platform to evaluate emerging methods for the segmentation and quantification of lung lesions caused by SARS-CoV-2 infection from CT images.

Where is VALDO?

Vascular Lesion Detection Challenge at MICCAI 2021

SegPC-2021

This challenge is positioned towards robust segmentation of cells which is the first stage to build such a tool for plasma cell cancer, namely, Multiple Myeloma (MM), which is a type of blood cancer.

EndoCV2021

Endoscopy Computer Vision Challenge 2021

Foot Ulcer Segmentation Challenge

PAIP2021

PAIP 2021 Challenge; Perineural invasion in multiple organ cancer (colon, prostate and pancreatobiliary tract)

NuCLS

Classification, Localization and Segmentation of nuclei in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad et al. 2021. arXiv:2102.09099 [cs.CV].

Breast Cancer Segmentation

Semantic segmentation of histologic regions in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083

DFUC2021

Diabetic Foot Ulcer Challenge 2021

WSSS4LUAD

The WSSS4LUAD dataset contains over 10,000 patches of lung adenocarcinoma from whole slide images from Guangdong Provincial People's Hospital and TCGA with image-level annotations. The goal of this challenge is to perform semantic segmentation for differentiating three important types of tissues in the WSIs of lung adenocarcinoma, including cancerous epithelial region, cancerous stroma region and normal region. Paticipants have to use image-level annotations to give pixel-level prediction.

MIDOG Challenge 2021

Mitosis Domain Generalization Challenge 2021 (part of MICCAI 2021)

AIROGS

Artificial Intelligence for RObust Glaucoma Screening Challenge

CoNIC 2022

Colon Nuclei Identification and Counting Challenge 2022

EndoCV2022

Developing methods for "detection task'' and ''segmentation task'' for endoscopic video sequence data

Parse2022

It is of significant clinical interest to study pulmonary artery structures in the field of medical image analysis. One prerequisite step is to segment pulmonary artery structures from CT with high accuracy and low time-consuming. The segmentation of pulmonary artery structures benefits the quantification of its morphological changes for diagnosis of pulmonary hypertension and thoracic surgery. However, due to the complexity of pulmonary artery topology, automated segmentation of pulmonary artery topology is a challenging task. Besides, the open accessible large-scale CT data with well labeled pulmonary artery are scarce (The large variations of the topological structures from different patients make the annotation an extremely challenging process). The lack of well labeled pulmonary artery hinders the development of automatic pulmonary artery segmentation algorithm. Hence, we try to host the first Pulmonary ARtery SEgmentation challenge in MICCAI 2022 (Named Parse2022) to start a new research topic.

TDSC-ABUS2023

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound

AGGC22

Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis

Multi-site, Multi-Domain Airway Tree Modeling (ATM’22)

Airway segmentation is a crucial step for the analysis of pulmonary diseases including asthma, bronchiectasis, and emphysema. The accurate segmentation based on X-Ray computed tomography (CT) enables the quantitative measurements of airway dimensions and wall thickness, which can reveal the abnormality of patients with chronic obstructive pulmonary disease (COPD). Besides, the extraction of patient-specific airway models from CT images is required for navigatiisted surgery.

Shifts Challenge 2022

The goal of the Shifts Challenge 2022 is to raise awareness among the research community about the problems of distributional shift, robustness, and uncertainty estimation, and to identify new solutions to address them. The competition will consist of two new tracks: White Matter Multiple Sclerosis (MS) lesion segmentation in 3D Magnetic Resonance Imaging (MRI) of the brain and Marine cargo vessel power estimation.

SEG.A. - Segmentation of the Aorta

Segmentation, modeling and visualization of the arterial tree are still a challenge in medical image analysis. The main track of this challenge deals with the fully automatic segmentation of the aortic vessel tree in computed tomography images. Optionally, teams can submit tailored solutions for meshing and visualization of the vessel tree.

OCELOT 2023: Cell Detection from Cell-Tissue Interaction