LUNA16

The LUNA16 challenge: automatic nodule detection on chest CT

Decathlon

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

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.

EndoCV2021

Endoscopy Computer Vision Challenge 2021

MIDOG Challenge 2021

Mitosis Domain Generalization Challenge 2021 (part of MICCAI 2021)

TIGER

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

autoPET

Automatic lesion segmentation in whole-body FDG-PET/CT

Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022

autoPET-II

Automated Lesion Segmentation in PET/CT - Domain Generalization

AutoPET III

Ischemic Stroke Lesion Segmentation Challenge 2024

The LUNA25 Challenge

HEad and neCK TumOR Lesion Segmentation, Diagnosis and Prognosis

HECKTOR 2025 is the next iteration of a medical imaging challenge focused on improving automated analysis of head and neck cancer using multimodal PET/CT data. The challenge features three complementary tasks that span the clinical workflow: automatic detection and segmentation of primary tumors and lymph nodes, prediction of recurrence-free survival using imaging and clinical data, and diagnosis of HPV status, which is crucial for treatment decisions. The 2025 edition significantly expands on previous challenges with a larger dataset exceeding, refined evaluation metrics that better assess both detection and segmentation capabilities, and the addition of radiotherapy planning dose maps as an information channel. This challenge aims to advance the development of clinical tools that can aid in treatment planning, outcome prediction, and diagnosis in head and neck cancer patients, ultimately supporting more personalized patient management approaches.

Deep-learning Evaluation for Enhanced Prognostics - PSMA PET

PANTHER Challenge

autoPET/CT IV

Automated Lesion Segmentation in PET/CT - The human frontier