PROSTATEx

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

INSTANCE2022

The 2022 Intracranial Hemorrhage Segmentation Challenge on Non-Contrast head CT (NCCT)

SynthRAD2023

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

Universal Lesion Segmentation Challenge '23

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.

REport Generation in pathology using Pan-Asia Giga-pixel WSIs

This project focuses on advancing automated pathology report generation using vision-language foundation models. It addresses the limitations of traditional NLP metrics (e.g., BLEU, METEOR, ROUGE) by emphasizing clinically relevant evaluation. The initiative includes standardized datasets, expert comparisons, and medical-domain-specific metrics to assess model performance. It also explores the integration of generated reports into diagnostic workflows with clinical feedback. To support fairness and generalizability, the challenge dataset comprises ~20,500 cases from six medical centers in Korea, Japan, India, Turkey, and Germany, promoting multicultural and multiethnic medical AI development.

cosas-test-phase