ARM : nnU-Net with Arena Mechanism for Medical Image Segmentation

The success of nnU-Net proves the significance of the rationality of workflow architecture and configuration settings in improving segmentation accuracy. However, since that, most efforts to improve U-Net have continued to address CNN inner limitations caused by architecture. These methods encountered challenges such as limited generalization, difficulty in managing data with varying distribution patterns. To tackle these issues, We designed a standardized processing workflow specifically tailored for the convolutional layers of U-Net : Arena Mechanism (ARM), inspired by game theory, which encompasses three stages: Recruit, Train and Fight. We augment the convolutional layers of U-Net with two additional processing branches (Recruit), serving as "challengers." These challengers are first optimized within a Hybrid Adaptive Weighting module to refine the feature representation of each internal channel (Train). During forward propagation, we design a cooperative loss and reward function to determine the optimal confidence distribution and fusion strategy for branch outputs (Fight). This standardized mechanism is designed to further nnU-Net’s core principle of "Automatic Adaptation." Experiments have demonstrated that this approach achieves state-of-the-art (SOTA) performance on both the ACDC, BraTS21 and KiTS datasets.