ScaleSurfer base all-data Stats Prediction Model
This repository contains a ScaleSurfer multi-head model for predicting FreeSurfer-style .stats targets from a T1w image and an aparc+aseg segmentation.
Files
stats_model.pt: full PyTorch stats checkpoint, including frozen encoder weights, prediction heads, target normalization, and target columns.config.json: architecture and feature-schema metadata needed byScaleSurferStatsPredictor.metadata.json: checksums and training/evaluation metadata.summary.csv,history.csv,target_metrics.csv: copied training diagnostics.
Test Summary
| Group | Targets | Values | Normalized MAE | Median absolute percent error |
|---|---|---|---|---|
| aseg | 119 | 39699 | 0.4714784485426931 | 7.629659175872803 |
| global | 64 | 16070 | 0.212405763403243 | 1.7018315196037292 |
| lh_aparc | 306 | 106742 | 0.4562009682892021 | 8.101013660430908 |
| rh_aparc | 306 | 106738 | 0.45695450692734807 | 7.982769966125488 |
Loading
from scalesurfer.stats import ScaleSurferStatsPredictor
predictor = ScaleSurferStatsPredictor.from_pretrained('base')
features = predictor.predict_subjects(subjects_dir, subjects, return_format="wide")
This model is intended for research workflows and is not a clinical diagnostic device.
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