# Experiment Status

## Current Method: ReMAP-PET

We use the name **ReMAP-PET** for the current Stage 1 method:

```text
Region-guided Metabolic Alignment with Partial-tuned PET encoders
```

The method trains a PET encoder by aligning 3D FDG-PET volumes with 120-region SUVR profiles. The key design is partial tuning of the high-level encoder block rather than treating the pretrained encoder as a fixed feature extractor.

The best current route is:

```text
MedicalNet 3D ResNet-50
+ layer4 partial tuning
+ PET-SUVR regression and contrastive alignment
```

Checkpoint on server:

```text
/data/Albus/Brain/runs/foundation/medicalnet_layer4_regalign_best.pt
```

Training command:

```bash
CUDA_VISIBLE_DEVICES=0 /data/Albus/miniconda3/bin/python -u scripts/train_pet_foundation.py \
  --backbone medicalnet \
  --encoder-train-scope layer4 \
  --epochs 50 \
  --batch-size 4 \
  --lr 1e-5 \
  --num-workers 2 \
  --output-size 96 96 96 \
  --contrastive-weight 0.2 \
  --regression-weight 1.0 \
  --out runs/foundation/medicalnet_layer4_regalign.pt \
  --best-out runs/foundation/medicalnet_layer4_regalign_best.pt
```

## Test Metrics

| Model | MAE | RMSE | Pearson | Spearman | PET->SUVR R@1 | SUVR->PET R@1 | Top5 High | Top5 Low |
|---|---:|---:|---:|---:|---:|---:|---:|---:|
| MedicalNet frozen MLP | 0.1173 | 0.1530 | 0.7390 | 0.8454 | 0.0261 | 0.0458 | 0.4288 | 0.7294 |
| BrainIAC frozen MLP | 0.1248 | 0.1614 | 0.7069 | 0.8150 | 0.0131 | 0.0196 | 0.3425 | 0.6797 |
| ReMAP-PET (MedicalNet layer4 partial tuning) | 0.0700 | 0.0894 | 0.9198 | 0.9350 | 0.7778 | 0.9216 | 0.6366 | 0.7660 |

## Interpretation

The current result supports partial tuning of the final MedicalNet stage as the primary Stage 1 strategy:

- it preserves MedicalNet's pretrained 3D medical representation;
- it adapts high-level features to FDG-PET metabolism;
- it improves both SUVR regression and PET-SUVR retrieval;
- it uses little GPU memory.

## Next Experiments

1. Add a region-token encoder instead of a flat SUVR MLP.
2. Add controlled text generated from SUVR tables.
3. Train PET-to-region-text contrastive alignment.
4. Add a loss-weight ablation:
   - regression only;
   - contrastive only;
   - contrastive 0.2 + regression 1.0;
   - contrastive 1.0 + regression 1.0.
5. Integrate BrainFM as a second foundation-model comparison.