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We will keep updating this repository for pretrained models and weights.
Pretrained neural networks for UK Biobank brain MRI images. SFCN, 3D-ResNet etc.
Under construction.
The models are trained, validated and benchmarked with UK Biobank brain MRI images, 14,503-subject release.
Model input shape: [batch_size, 1, 160, 192, 160]
| File | Model | No. training subjects | Test MAE (years) | Validation MAE (yrs) | Train MAE (yrs) | Val-Train MAE gap (yrs) |
|---|---|---|---|---|---|---|
| ./brain_age/run_20190719_00_epoch_best_mae.p | SFCN (SGD) | 12,949 | 2.14±0.05 | 2.18±0.04 | 1.36±0.03 | 0.83±0.06 |
(As summarized in Table 1 in the manuscript)
Checkout the file examples.ipynb
model = SFCN()
model = torch.nn.DataParallel(model)
# This is to be modified with the path of saved weights
p_ = './run_20190719_00_epoch_best_mae.p'
model.load_state_dict(torch.load(p_))- UK Biobank preprocessing information https://www.fmrib.ox.ac.uk/ukbiobank/fbp/
Accurate brain age prediction with lightweight deep neural networks Han Peng, Weikang Gong, Christian F. Beckmann, Andrea Vedaldi, Stephen M Smith Medical Image Analysis (2021); doi: https://doi.org/10.1016/j.media.2020.101871