{
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        "arch_kwargs": null,
        "arch_kwargs_requiring_import": null
    },
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                "resampling_fn_data": "resample_data_or_seg_to_shape",
                "resampling_fn_data_kwargs": {
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    "citations": [
        {
            "type": "Architecture",
            "name": "ResEncL",
            "apa_citations": [
                "Isensee, F., Wald, T., Ulrich, C., Baumgartner, M., Roy, S., Maier-Hein, K., & Jaeger, P. F. (2024, October). nnu-net revisited: A call for rigorous validation in 3d medical image segmentation. MICCAI."
            ]
        },
        {
            "type": "Pretraining Method",
            "name": "Volume Fusion",
            "apa_citations": [
                "Wang, G., Wu, J., Luo, X., Liu, X., Li, K., & Zhang, S. (2023). Mis-fm: 3d medical image segmentation using foundation models pretrained on a large-scale unannotated dataset. arXiv preprint arXiv:2306.16925."
            ]
        },
        {
            "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": "VolumeFusionTrainer_BS8_lr_1e3"
}