  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.

Image and Video Processing;Computer Vision and Pattern Recognition