{
  "_ilex": {
    "architecture": "ilex.models.brats_segresnet.model.BraTSSegResNet",
    "constructor_kwargs": {
      "in_channels": 4,
      "out_channels": 3
    },
    "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": "Myronenko A.; MONAI Consortium",
  "copyright": "Network architecture and pretrained weights: copyright (c) MONAI Consortium, released under the Apache-2.0 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": "MONAI brats_mri_segmentation (SegResNet; Myronenko 2018), ported to JAX / Equinox from the upstream PyTorch bundle. A ResNet-style 3D encoder/decoder (pre-activation GroupNorm+ReLU+Conv residual blocks, strided-conv downsampling, additive skips, non-trainable trilinear upsampling) that segments the three BraTS tumour sub-regions from a 4-channel multimodal MRI volume. The VAE branch used for autoencoder-regularised training is out of scope; this port is the segmentation forward only.",
  "equinox_version": "0.13.8",
  "ilex_version": "0.0.0.dev0",
  "image_classes": "4-channel 3D MRI (channel 0 T1c, 1 T1, 2 T2, 3 FLAIR).",
  "intended_use": "Research. Brain-tumour sub-region segmentation from 4-channel multimodal MRI (T1c, T1, T2, FLAIR), per-channel nonzero z-score normalised, with each spatial dimension a multiple of 8. The bundle is trained / evaluated on the BraTS dataset.",
  "jax_version": "0.10.0",
  "label_classes": "Three overlapping (multi-label) tumour sub-regions: channel 0 tumour core (TC), channel 1 whole tumour (WT), channel 2 enhancing tumour (ET).",
  "network_data_format": {
    "inputs": {},
    "outputs": {}
  },
  "numpy_version": "2.4.4",
  "pred_classes": "3 raw logit channels (TC, WT, ET). Apply a per-channel sigmoid and threshold at 0.5; the regions overlap, so this is multi-label, not a softmax simplex.",
  "references": [
    "Myronenko A. (2018). 3D MRI brain tumor segmentation using autoencoder regularization. BrainLes 2018 (MICCAI workshop). arXiv:1810.11654. https://arxiv.org/abs/1810.11654",
    "MONAI Model Zoo: brats_mri_segmentation. https://github.com/Project-MONAI/model-zoo/tree/dev/models/brats_mri_segmentation",
    "Bundle weights: https://huggingface.co/MONAI/brats_mri_segmentation"
  ],
  "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
  "task": "Multimodal brain-tumour sub-region segmentation (BraTS)",
  "version": "0.0.0"
}