| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - other |
| language: |
| - en |
| pretty_name: BrainAge Golden Preprocessed Cache |
| size_categories: |
| - 1K<n<10K |
| tags: |
| - neuroimaging |
| - mri |
| - brain-age |
| - pytorch |
| - preprocessed |
| - tensor-cache |
| --- |
| |
| # BrainAge Golden Preprocessed Cache |
|
|
| **6,050 preprocessed brain MRI tensors** ready for training a brain-age |
| prediction model. Skip the 40+ hour preprocessing step and jump straight |
| to model training. |
|
|
| ## What's inside |
|
|
| Each `.pt` file (one per subject) contains: |
|
|
| | Key | Type | Shape | Description | |
| |-----|------|-------|-------------| |
| | `volume` | float16 | (128, 144, 112) | Z-normed T1w brain in MNI space, trilinear-resized | |
| | `tab` | float32 | (86,) | 70 regional volumes (log1p/12) + 3 sex one-hot + 13 site one-hot | |
| | `age` | float32 | scalar | Chronological age in years | |
| | `meta` | dict | — | subject_id, site, sex, age, split | |
| |
| ## Stats |
| |
| | Metric | Value | |
| |--------|-------| |
| | Total subjects | 6,050 | |
| | Age range | 0 – 86 years | |
| | Source datasets | 12 (BCP, Calgary, ds002726, ds000248, PTBP, IXI, MPI-Leipzig, AOMIC, NKI-Rockland, ABIDE-I, ABIDE-II, ADHD-200) | |
| | Volume shape | 128 × 144 × 112 (D × H × W) | |
| | Tabular dim | 86 (70 regions + 3 sex + 13 site) | |
| | File size | ~4 MB each | |
| | Total size | ~24 GB | |
| |
| ## Preprocessing pipeline applied |
| |
| ``` |
| Raw T1w NIfTI |
| → HD-BET skull-strip (GPU) |
| → N4 bias correction (ANTs) |
| → Affine registration to MNI152 1mm |
| → Z-score intensity normalization |
| → Harvard-Oxford atlas segmentation (69 regions) |
| → Volume measurement + rescaling to native space |
| → Tensor packaging (.pt) |
| ``` |
| |
| ## Quick start |
| |
| ```python |
| from huggingface_hub import snapshot_download |
| import torch |
| |
| # Download (~24 GB) |
| snapshot_download( |
| "bilalahmad176176/BrainAge-Golden-Preprocessed", |
| repo_type="dataset", |
| local_dir="cache/" |
| ) |
| |
| # Load one subject |
| data = torch.load("cache/cache/IXI002.pt", weights_only=False) |
| print(data["volume"].shape) # (128, 144, 112) float16 |
| print(data["tab"].shape) # (86,) float32 |
| print(data["age"]) # e.g. 36.2 |
| print(data["meta"]) # {'subject_id': 'IXI002', 'site': 'DataSet-6_IXI', ...} |
| ``` |
| |
| ## Train a model |
| |
| ```bash |
| # Generate split |
| python -m pipeline_v2.data_split \ |
| --manifests Golden-0-to-25/manifest.csv Golden-25plus/manifest.csv \ |
| --out cache/split.csv |
| |
| # Train |
| python -m pipeline_v2.train \ |
| --cache_dir cache/cache \ |
| --split_csv cache/split.csv \ |
| --out_ckpt brainage_sfcn.pt \ |
| --epochs 60 --batch 4 |
| ``` |
| |
| ## Related |
|
|
| - Raw dataset: [bilalahmad176176/BrainAge-Golden-Raw](https://huggingface.co/datasets/bilalahmad176176/BrainAge-Golden-Raw) |
| - 3D Viewer demo: [bilalahmad176176/BrainAge-3D-Viewer](https://huggingface.co/spaces/bilalahmad176176/BrainAge-3D-Viewer) |
|
|
| ## Citation |
|
|
| Please cite the original source studies listed in the raw dataset manifests. |
|
|
