We participate in the AutoPET II challenge by modifying nnU-Net only through its easy to understand and modify 'nnUNetPlans.json' file. By switching to a UNet with residual encoder, increasing the batch size and increasing the patch size we obtain a configuration that substantially outperforms the automatically configured nnU-Net baseline (5-fold cross-validation Dice score of 65.14 vs 33.28) at the expense of increased compute requirements for model training. Our final submission ensembles the two most promising configurations.

翻译：我们参与AutoPET II挑战的方式是仅通过修改nnU-Net易于理解与调整的'nnUNetPlans.json'文件来实现。通过切换为带有残差编码器的UNet、增加批量大小以及扩大图像块尺寸，我们获得了一个在性能上显著优于自动配置的nnU-Net基线模型（5折交叉验证的Dice得分为65.14 vs 33.28）的配置，但代价是模型训练的计算需求增加。我们的最终提交方案集成了两个最具潜力的配置。