Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON plans
- Resource Type
- Working Paper
- Authors
- Isensee, Fabian; Maier-Hein, Klaus H.
- Source
- Subject
- Electrical Engineering and Systems Science - Image and Video Processing
Computer Science - Computer Vision and Pattern Recognition
- Language
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.