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{
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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": "SimCLR",
"apa_citations": [
"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020, November). A simple framework for contrastive learning of visual representations. ICML."
]
},
{
"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": "SimCLRTrainer_BS32"
} |