| # Experiment Status |
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| ## Current Method: ReMAP-PET |
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| We use the name **ReMAP-PET** for the current Stage 1 method: |
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| ```text |
| Region-guided Metabolic Alignment with Partial-tuned PET encoders |
| ``` |
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| 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. |
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| The best current route is: |
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| ```text |
| MedicalNet 3D ResNet-50 |
| + layer4 partial tuning |
| + PET-SUVR regression and contrastive alignment |
| ``` |
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| Checkpoint on server: |
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| ```text |
| /data/Albus/Brain/runs/foundation/medicalnet_layer4_regalign_best.pt |
| ``` |
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| Training command: |
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| ```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 |
| ``` |
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| ## Test Metrics |
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| | 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 | |
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| ## Interpretation |
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| The current result supports partial tuning of the final MedicalNet stage as the primary Stage 1 strategy: |
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| - 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. |
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| ## Next Experiments |
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| 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. |
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|
