docs/PAPER_EXPERIMENT_PLAN.md

ReMAP-PET Paper and Experiment Plan

Working Title

ReMAP-PET: Region-guided Metabolic Alignment with Partial-tuned PET Encoders

Central Claim

FDG-PET should not be treated only as an image classification input. Its paired 120-region SUVR profile provides structured metabolic semantics. ReMAP-PET uses this region-level supervision to partially adapt a pretrained 3D medical/brain encoder into a PET metabolic encoder.

The core claim is:

High-level partial tuning plus regional metabolic alignment produces a stronger FDG-PET representation than frozen probing, and it gives a usable bridge from 3D PET to structured metabolic language.

Paper Structure

1. Introduction

Motivation:

• FDG-PET reflects glucose metabolism rather than anatomy.
• Existing brain MRI foundation models are not directly optimized for PET metabolic semantics.
• Current PET work often focuses on diagnosis labels, while our data provides richer region-level SUVR supervision.

Main contribution:

• Define PET-to-regional metabolic alignment.
• Propose ReMAP-PET, a partial-tuned PET encoder trained with SUVR regression and PET-SUVR contrastive alignment.
• Evaluate against frozen pretrained encoders and newer brain/neuroimaging foundation encoders.
• Show interpretable region-level prediction and retrieval.

2. Related Work

Groups to cover:

• Brain MRI foundation models: BrainIAC, BrainFound, BrainDINO.
• General clinical neuroimaging foundation models: NeuroVFM.
• 3D MRI vision-language models: Decipher-MR.
• Generative multimodal brain models: GenBrain.
• FDG-PET and AD metabolic imaging.
• Vision-language / CLIP-style contrastive alignment.

3. Dataset and Task

Dataset:

• 1015 paired FDG-PET volumes and SUVR CSV tables.
• 120 real brain regions after excluding Background.
• Fixed split: 710 train, 152 val, 153 test.

Tasks:

• PET-to-SUVR regression.
• PET-SUVR bidirectional retrieval.
• High/low metabolic region identification.
• Controlled region-text alignment in the later stage.

4. Method

ReMAP-PET Stage 1:

3D FDG-PET
  -> pretrained 3D encoder
  -> partial tuning of high-level block
  -> PET embedding
  -> SUVR regression head
  -> contrastive alignment with SUVR embedding

Training loss:

L = lambda_reg * L_suvr
  + lambda_con * L_pet_suvr_contrastive

Main implementation:

• PET encoder: MedicalNet 3D ResNet-50.
• Partial tuning: only layer4.
• Current best: lambda_reg=1.0, lambda_con=0.2.

5. Experiments

Table 1: Dataset Summary

Purpose:

• Show what data we have and why the task is not ordinary classification.

Columns:

• Dataset
• Samples
• PET format
• SUVR regions
• Train / Val / Test
• Labels available
• Task supported

Rows:

• Our FDG-PET-SUVR dataset.
• External ADNI/OASIS-3 if acquired later.

Table 2: Main Stage 1 Internal Test Results

Purpose:

• Main quantitative result on the held-out test set.

Rows:

• MedicalNet frozen MLP.
• BrainIAC frozen MLP.
• ReMAP-PET / MedicalNet layer4 partial tuning.
• BrainFound.
• NeuroVFM encoder.
• Decipher-MR.
• BrainDINO.
• GenBrain encoder.

Metrics:

• MAE
• RMSE
• Pearson
• Spearman
• PET->SUVR R@1 / R@5
• SUVR->PET R@1 / R@5
• MRR
• Median Rank
• Top5 High
• Top5 Low

Table 3: Modern Encoder Comparison

Purpose:

• Show whether newer brain/neuroimaging foundation encoders transfer better than the current ReMAP-PET backbone.

Rows:

• BrainFound.
• NeuroVFM encoder.
• Decipher-MR.
• BrainDINO.
• GenBrain encoder.
• ReMAP-PET as reference.

Settings:

• Frozen probing.
• Adapter tuning if feasible.
• High-level partial tuning if the architecture supports it.

Primary metrics:

• MAE
• Pearson
• PET->SUVR R@1
• SUVR->PET R@1
• Top5 Low

Table 4: ReMAP-PET Ablation

Purpose:

• Justify the ReMAP-PET training objective and show why the chosen partial-tuned alignment design works.

Rows:

• Regression only: lambda_con=0.0, lambda_reg=1.0.
• Weak contrastive: lambda_con=0.1, lambda_reg=1.0.
• Current setting: lambda_con=0.2, lambda_reg=1.0.
• Strong contrastive: lambda_con=0.5, lambda_reg=1.0.
• Contrastive-heavy: lambda_con=1.0, lambda_reg=1.0.

Metrics:

• MAE
• RMSE
• Pearson
• Spearman
• PET->SUVR R@1
• SUVR->PET R@1
• Top5 High
• Top5 Low

Expected interpretation:

• Regression-only gives the best numeric SUVR prediction but fails at PET-SUVR retrieval.
• Stronger contrastive weights greatly improve retrieval but gradually hurt numeric SUVR prediction.
• The balanced ReMAP-PET setting should be selected by the intended Stage 1 target: metabolic prediction plus cross-modal alignment, not regression alone.

Current test results are stored in:

docs/REMAP_PET_ABLATION_RESULTS.csv

Main observation:

lambda_con=0.0 -> MAE 0.0549, PET->SUVR R@1 0.0065
lambda_con=0.2 -> MAE 0.0700, PET->SUVR R@1 0.7778
lambda_con=0.5 -> MAE 0.0766, PET->SUVR R@1 0.8366

This means pure regression is not enough for the paper goal. ReMAP-PET needs contrastive alignment because the paper claims a PET-to-regional-metabolic semantic space, not only SUVR curve fitting.

Table 5: External Generalization

Purpose:

• Show the learned PET metabolic encoder is not only fitting our internal dataset.

Candidate external datasets:

• ADNI FDG-PET with PET Core SUVR summaries.
• OASIS-3 FDG-PET with post-processed PET outputs.

Rows:

• Frozen MedicalNet.
• ReMAP-PET.
• Best modern encoder variant.

Metrics:

• If SUVR available: MAE, RMSE, Pearson, Spearman.
• If diagnosis labels available: linear probe AUROC / balanced accuracy.
• Retrieval if paired PET-SUVR is available.

Table 6: Region Interpretability

Purpose:

• Demonstrate clinical interpretability at the brain-region level.

Rows:

• Model variants.

Metrics:

• Top5 Low overlap.
• Top5 High overlap.
• Region mention precision.
• Region mention recall.
• Example low-metabolism regions recovered.

6. Figures

Figure 1: Method Overview

Show:

3D PET -> partial-tuned PET encoder -> PET embedding
SUVR table -> region encoder -> SUVR embedding
PET embedding <-> SUVR embedding contrastive alignment
PET embedding -> 120-region SUVR prediction

Figure 2: Embedding Retrieval Visualization

Show:

• PET-SUVR similarity matrix.
• Correct pairs on diagonal.
• Compare frozen MedicalNet vs ReMAP-PET.

Figure 3: Region-level Case Study

Show:

• Predicted vs true top low-metabolism regions.
• Controlled text generated from true/predicted SUVR.
• Example similar-case retrieval.

Figure 4: Ablation Tradeoff Curve

Show:

• x-axis: contrastive weight.
• y-axis 1: MAE.
• y-axis 2: PET->SUVR Recall@1.

Experiment Execution Order

1. Finish ReMAP-PET loss-weight ablation.
2. Evaluate each best checkpoint on the internal test set.
3. Integrate the five chosen modern encoders:
   • BrainFound.
   • NeuroVFM encoder.
   • Decipher-MR.
   • BrainDINO.
   • GenBrain encoder.
4. Run frozen probing first for every modern encoder.
5. Add adapter or high-level partial tuning only where the architecture and weights are stable.
6. Add external validation on ADNI or OASIS-3.

What Not To Claim

Do not claim:

• We solve clinical AD diagnosis directly.
• We train a full PET foundation model from scratch.
• We generate free-form radiology reports.

Safer claim:

ReMAP-PET learns a region-grounded FDG-PET metabolic representation that supports SUVR prediction, PET-SUVR retrieval, and interpretable metabolic region analysis.