File size: 3,597 Bytes
dfbb889 d14eed8 8b2ca9c d14eed8 8b2ca9c d14eed8 8b2ca9c d14eed8 8b2ca9c d14eed8 dfbb889 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
{
"architecture_plans": {
"arch_class_name": "PrimusM",
"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": [
160,
160,
160
]
}
},
"experiment_planner_used": "FixedResEncUNetPlanner"
},
"pretrain_num_input_channels": 1,
"recommended_downstream_patchsize": [
160,
160,
160
],
"key_to_encoder": "eva",
"key_to_stem": "down_projection",
"keys_to_in_proj": [
"down_projection.proj"
],
"key_to_lpe": "eva.pos_embed",
"citations": [
{
"type": "Architecture",
"name": "PrimusM",
"apa_citations": [
"Wald, T., Roy, S., Isensee, F., Ulrich, C., Ziegler, S., Trofimova, D., ... & Maier-Hein, K. (2025). Primus: Enforcing attention usage for 3d medical image segmentation. arXiv preprint arXiv:2503.01835."
]
},
{
"type": "Pretraining Method",
"name": "Masked Image Modeling",
"apa_citations": [
"Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., ... & Hu, H. (2022). Simmim: A simple framework for masked image modeling. CVPR."
]
},
{
"type": "Pre-Training Dataset",
"name": "OpenMind",
"apa_citations": [
"Wald, T., Ulrich, C., Suprijadi, J., Ziegler, S., Nohel, M., Peretzke, R., ... & Maier-Hein, K. H. (2024). An OpenMind for 3D medical vision self-supervised learning. arXiv preprint arXiv:2412.17041."
]
},
{
"type": "Framework",
"name": "nnssl",
"apa_citations": [
"Wald, T., Ulrich, C., Lukyanenko, S., Goncharov, A., Paderno, A., Maerkisch, L., ... & Maier-Hein, K. (2024). Revisiting MAE pre-training for 3D medical image segmentation. CVPR."
]
}
],
"trainer_name": "SimMIMEvaTrainer_BS8"
} |