{ "architecture_plans": { "arch_class_name": "ResEncL", "arch_kwargs": null, "arch_kwargs_requiring_import": null }, "pretrain_plan": { "dataset_name": "Dataset745_OpenNeuro_v2", "plans_name": "nnsslPlans", "original_median_spacing_after_transp": [ 1, 1, 1 ], "image_reader_writer": "SimpleITKIO", "transpose_forward": [ 0, 1, 2 ], "transpose_backward": [ 0, 1, 2 ], "configurations": { "onemmiso": { "data_identifier": "nnsslPlans_3d_fullres", "preprocessor_name": "DefaultPreprocessor", "spacing_style": "onemmiso", "normalization_schemes": [ "ZScoreNormalization" ], "use_mask_for_norm": [ false ], "resampling_fn_data": "resample_data_or_seg_to_shape", "resampling_fn_data_kwargs": { "is_seg": false, "order": 3, "order_z": 0, "force_separate_z": null }, "resampling_fn_mask": "resample_data_or_seg_to_shape", "resampling_fn_mask_kwargs": { "is_seg": true, "order": 1, "order_z": 0, "force_separate_z": null }, "spacing": [ 1, 1, 1 ], "patch_size": [ 64, 64, 64 ] } }, "experiment_planner_used": "FixedResEncUNetPlanner" }, "pretrain_num_input_channels": 1, "recommended_downstream_patchsize": [ 160, 160, 160 ], "key_to_encoder": "encoder.stages", "key_to_stem": "encoder.stem", "keys_to_in_proj": [ "encoder.stem.convs.0.conv", "encoder.stem.convs.0.all_modules.0" ], "key_to_lpe": null, "citations": [ { "type": "Architecture", "name": "ResEncL", "bibtex_citations": [ "@inproceedings{isensee2024nnu,\n title={nnu-net revisited: A call for rigorous validation in 3d medical image segmentation},\n author={Isensee, Fabian and Wald, Tassilo and Ulrich, Constantin and Baumgartner, Michael and Roy, Saikat and Maier-Hein, Klaus and Jaeger, Paul F},\n booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},\n pages={488--498},\n year={2024},\n organization={Springer}\n }" ] }, { "type": "Pretraining Method", "name": "Masked Auto Encoder", "bibtex_citations": [ "@article{wald2024revisiting,\n title={Revisiting MAE pre-training for 3D medical image segmentation},\n author={Wald, Tassilo and Ulrich, Constantin and Lukyanenko, Stanislav and Goncharov, Andrei and Paderno, Alberto and Maerkisch, Leander and J{\"a}ger, Paul F and Maier-Hein, Klaus},\n journal={arXiv preprint arXiv:2410.23132},\n year={2024}\n}" ] }, { "type": "Pre-Training Dataset", "name": "OpenMind", "bibtex_citations": [ "@article{wald2024openmind,\n title={An OpenMind for 3D medical vision self-supervised learning},\n author={Wald, Tassilo and Ulrich, Constantin and Suprijadi, Jonathan and Ziegler, Sebastian and Nohel, Michal and Peretzke, Robin and K{\"o}hler, Gregor and Maier-Hein, Klaus H},\n journal={arXiv preprint arXiv:2412.17041},\n year={2024}\n }\n " ] }, { "type": "Framework", "name": "nnssl", "bibtex_citations": [ "@article{wald2024revisiting,\n title={Revisiting MAE pre-training for 3D medical image segmentation},\n author={Wald, Tassilo and Ulrich, Constantin and Lukyanenko, Stanislav and Goncharov, Andrei and Paderno, Alberto and Maerkisch, Leander and J{\"a}ger, Paul F and Maier-Hein, Klaus},\n journal={arXiv preprint arXiv:2410.23132},\n year={2024}\n}" ] } ], "trainer_name": "BaseMAETrainer_BS8" }