{
  "_ilex": {
    "architecture": "ilex.models.triad.swin_model.TriadSwinViT",
    "constructor_kwargs": {
      "input_channels": 1
    },
    "format": "ilex",
    "framework_version": {
      "equinox": "0.13.8",
      "ilex": "0.0.0.dev0",
      "jax": "0.10.0",
      "jaxlib": "0.10.0",
      "numpy": "2.4.4",
      "safetensors": "0.7.0"
    },
    "has_state": false,
    "origin": "ilex-native"
  },
  "authors": "Wang S., et al.",
  "copyright": "Network architecture and pretrained weights: copyright (c) the Triad authors, released under the MIT License. JAX / Equinox port: copyright (c) the ilex authors, released under the Apache-2.0 / GPL-3.0 dual license used by ilex itself.",
  "data_type": "nibabel",
  "description": "Triad vision foundation model for 3D MRI, ported to JAX / Equinox from the upstream PyTorch release. Triad is an nnUNet PlainConvEncoder pretrained self-supervised on Triad-131K (131,170 3D MRI volumes spanning brain, breast, and prostate; T1/T2/FLAIR/DWI/fMRI/DCE) and serves as a transfer-learning backbone for downstream MRI segmentation, classification, and registration. The published checkpoints are encoder-only (the self-supervised decoder / mask token are stripped); this port exposes the pretrained encoder, whose multi-scale features are the transfer representation. Two backbone families are ported: the nnUNet PlainConvUNet encoder (TriadPlainConvUNet) and the 3D Swin Transformer encoder (TriadSwinViT, the Swin-B variant, via the shared nimox SwinViT primitive). Each is released under two self-supervised objectives -- masked autoencoding (MAE) and SimMIM -- as separate bundles (four in total).",
  "equinox_version": "0.13.8",
  "ilex_version": "0.0.0.dev0",
  "image_classes": "Single-channel 3D MRI volume (contrast-general; pretrained across T1, T2, FLAIR, DWI, fMRI, DCE).",
  "intended_use": "Research. A pretrained 3D-MRI encoder backbone for transfer learning; consumers attach a task-specific decoder / head and fine-tune. Inputs are single-channel 3D MRI volumes with each spatial dimension a multiple of 32.",
  "jax_version": "0.10.0",
  "label_classes": "N/A -- self-supervised backbone; no fixed label set. Output is the tuple of per-stage encoder feature maps.",
  "network_data_format": {
    "inputs": {},
    "outputs": {}
  },
  "numpy_version": "2.4.4",
  "pred_classes": "Multi-scale encoder skip features (6 stages, channels [32, 64, 128, 256, 320, 320]); the bottleneck is the deepest skip.",
  "references": [
    "Wang S., et al. (2025). Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging. arXiv:2502.14064. https://arxiv.org/abs/2502.14064",
    "Codebase: https://github.com/wangshansong1/Triad"
  ],
  "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
  "task": "3D-MRI self-supervised foundation backbone (transfer learning)",
  "version": "0.0.0"
}