- Grand Challenge

Mingyuan Meng

MungoMeng

Australia
The University of Sydney
School of Computer Science

Statistics

Member for 2 years, 11 months
76 challenge submissions

Activity Overview

Learn2Reg

Challenge User

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

MICCAI HECKTOR 2022

Challenge User

Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

SegRap 2023

Challenge User

A segmentation challenge with 200 patients (two modalities of CT images, 45 OARs and 2 GTVs).

HEad and neCK TumOR Lesion Segmentation, Diagnosis and Prognosis

Challenge User

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.