| { |
| "_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" |
| } |
