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Academic Achievements
Led development of STU-Net: the largest pre-trained medical image segmentation model (1.4B parameters) based on the largest public dataset (>100k annotations) as of April 2023
A-Eval accepted by MedIA 2025, establishing a cross-dataset and cross-modality benchmark for abdominal multi-organ segmentation
UNet-Benchmark study published in Scientific Reports 2025, revisiting model scaling strategies for 3D medical image segmentation with U-Net
Champion of MICCAI FLARE22 Challenge and FLARE24 Task2 Challenge
Open-sourced multiple medical segmentation projects on GitHub with significant community engagement (292+ stars)