nnU Net

nnU-Net is a self-configuring deep learning framework designed for 3D medical image segmentation, aiming to achieve high accuracy and efficiency across diverse datasets without extensive manual hyperparameter tuning. Current research focuses on enhancing nnU-Net's performance through architectural modifications, such as incorporating multi-scale features and dynamic convolutions, and by addressing challenges like noisy labels and uncertainty quantification. Its success stems from its robust baseline performance and ease of use, making it a valuable tool for various medical image analysis tasks and a benchmark for evaluating novel segmentation methods.