# TriFuse-AD: An Honest Multimodal Benchmark for Three-Stage Dementia Staging on OASIS-1 Structural MRI

## Abstract

We present a controlled, leakage-free benchmark for three-stage cognitive
classification — cognitively normal (CN), very mild dementia (VMD, CDR=0.5), and
Alzheimer's dementia (AD, CDR≥1) — on the OASIS-1 cross-sectional cohort, and we
propose **TriFuse-AD**, a tri-planar CNN + slice-plane Transformer with gated
demographic fusion. On an age-restricted cohort (≥60 years, 198 subjects) evaluated
with subject-level repeated stratified 5-fold cross-validation (3 seeds, 15
evaluations per model), we benchmark eleven models spanning tabular, 2D/2.5D/3D
CNN, Vision Transformer, hybrid, and multimodal families, and report Macro-F1 with
bootstrap confidence intervals as the primary metric.

Our central finding is a *negative* but informative one: on this small, confounded
cohort no MRI-only deep network surpasses a plain tabular XGBoost on morphometric
and demographic features (Macro-F1 0.474), and TriFuse-AD (0.488 ± 0.066) does not
significantly outperform a trivial DenseNet late-concatenation baseline (0.497 ±
0.062; paired permutation p = 0.55). A confound analysis shows a structured-feature
model using whole-brain volume, intracranial volume and demographics alone reaches
Macro-F1 0.480, confirming that most of the recoverable signal on OASIS-1 is
morphometric rather than learned from raw voxels. We report these results
transparently, together with per-class, per-subgroup, ablation, and interpretability
analyses, as a cautionary and reproducible reference point for small-cohort
structural-MRI dementia staging. We make no clinical, diagnostic, state-of-the-art,
or cross-site claims.

**Keywords:** Alzheimer's disease, dementia staging, OASIS-1, structural MRI,
multimodal fusion, confounding, small-sample benchmark, Macro-F1.


## 1. Introduction

Automated staging of cognitive decline from structural MRI is an attractive goal:
T1-weighted scans are cheap, ubiquitous, and carry well-documented atrophy signatures
of Alzheimer's disease (AD). A large literature reports high accuracy for AD-vs-CN
classification, and a growing body extends this to intermediate stages. Yet much of
this literature is difficult to compare or trust, for three recurring reasons.

First, **label leakage and target-adjacent inputs.** Clinical staging labels such as
the Clinical Dementia Rating (CDR) or Mini-Mental State Exam (MMSE) are sometimes
fed to the model, directly or through hand-picked features, inflating performance in
a way that would not survive prospective use.

Second, **subject-level leakage.** When multiple scans of the same subject, or
augmented slices from one volume, are split across train and test, the reported
accuracy measures memorization of subjects rather than generalization.

Third, **confounding by age and head size.** Age is strongly correlated with both
brain atrophy and dementia stage; a model that merely reads off age (or a proxy such
as whole-brain volume) can appear to "diagnose from MRI" while learning nothing
disease-specific.

This paper takes the opposite stance to the accuracy-maximizing literature. We build
a deliberately conservative benchmark on the OASIS-1 cross-sectional cohort and ask a
narrower, more honest question: *under leakage-free, age-restricted, subject-level
evaluation, how much three-stage signal is actually recoverable from structural MRI,
and does architectural sophistication help beyond trivial baselines?*

Concretely, our contributions are:

1. **A reproducible, leakage-free three-stage protocol** on OASIS-1: CN / VMD / AD
   from CDR, with an age≥60 restriction to attenuate the age shortcut, one volume
   per subject, subject-level repeated stratified cross-validation (15 evaluations),
   and CDR/MMSE excluded from all model inputs. Macro-F1 with bootstrap CIs is the
   primary metric, not accuracy, because the classes are imbalanced (98/70/30).

2. **A broad, identically-trained model zoo** — eleven models from tabular XGBoost to
   3D Swin Transformers and two recent CNN-Transformer hybrids, all retrained on our
   folds so numbers are mutually comparable and never copied across datasets.

3. **TriFuse-AD**, a tri-planar shared-CNN encoder with a slice-plane Transformer and
   a gated demographic-fusion head, presented not as a winner but as a well-specified
   point in the design space that we ablate component-by-component.

4. **An explicit confound and subgroup analysis** quantifying how much of the signal
   is available from demographics and morphometry alone, and how performance varies
   across age bands and sex.

Our results are sobering. Multimodal fusion helps — the two top models both fuse MRI
with structured features — but TriFuse-AD's gated tri-planar attention does not beat
a plain late-concatenation baseline, and no MRI-only network beats tabular XGBoost.
We argue that reporting this clearly is more useful to the field than another
incremental accuracy number obtained under looser controls.


## 2. Related Work

**Deep learning for AD classification.** Convolutional networks on 2D slices, 2.5D
slice stacks, and full 3D volumes have all been applied to ADNI and OASIS. 3D CNNs
(e.g., 3D ResNet, DenseNet variants) model volumetric context directly but are
data-hungry; 2.5D approaches trade volumetric completeness for ImageNet-pretrained
2D backbones and lower memory. Vision Transformers and 3D Swin Transformers have more
recently been applied to brain MRI, and hybrid CNN-Transformer designs (compact HCCT,
CNN-VSwinFormer) aim to combine local inductive bias with global attention. Reported
accuracies vary widely and are frequently not comparable because of differing
cohorts, label definitions, and validation protocols.

**Multimodal fusion.** Combining imaging with tabular clinical/demographic data is
well established, from early concatenation to gated and attention-based fusion. On
small cohorts, however, the marginal value of sophisticated fusion over simple
concatenation is rarely tested with matched training and statistics.

**Confounding and evaluation critique.** A recurrent methodological thread warns that
age, sex, and head-size confounds, together with subject-level leakage, can dominate
apparent MRI-based performance, and that accuracy on imbalanced stages is misleading.
Our work is squarely in this tradition: rather than proposing a higher number, we
instrument the benchmark to expose how much signal is confound-driven, and we hold
every model to the same leakage-free protocol.

**OASIS-1.** The OASIS cross-sectional release provides T1-weighted scans with CDR,
MMSE, and morphometric summaries (eTIV, nWBV, ASF) for a demographically broad adult
cohort. It is smaller and less standardized than ADNI, which makes it a realistic
stress test for small-sample generalization — and a setting where honest reporting of
variance and confounding matters most.


## 3. Data and Cohort

**Source.** We use the OASIS-1 cross-sectional release (416 subjects, ages 18–96).
Each subject has T1-weighted MPRAGE scans and an atlas-registered, brain-masked,
gain-field-corrected volume (`*_111_t88_masked_gfc`, 176×208×176, 1 mm isotropic),
along with demographic and morphometric summaries: age, sex, education, socioeconomic
status (SES), estimated total intracranial volume (eTIV), normalized whole-brain
volume (nWBV), atlas scaling factor (ASF), MMSE, and CDR. We reconstruct the
per-subject metadata from the individual OASIS subject records.

**Labels.** We map CDR to three cognitive stages:

| CDR | Stage | Meaning |
|-----|-------|---------|
| 0   | CN    | cognitively normal |
| 0.5 | VMD   | very mild dementia |
| ≥1  | AD    | Alzheimer's dementia |

We deliberately name CDR=0.5 **very mild dementia (VMD)**, not "MCI": the CDR
operationalization is not equivalent to a clinical MCI diagnosis, and conflating them
would overstate clinical relevance. **CDR and MMSE are never used as model inputs** —
they define or correlate with the label and would constitute leakage. They are
retained only for cohort description and confound analysis.

**Age restriction.** Because age is the dominant confound (young CN subjects are
trivially separable from older AD subjects on brain size alone), we restrict to
**age ≥ 60**. This removes the easy young-CN population and forces models to
discriminate stages within an older cohort where atrophy overlaps across stages.

**Final cohort.** The age≥60 filter yields **198 subjects**: CN = 98, VMD = 70,
AD = 30. Mean age 76.3 ± 8.1; 131 male / 67 female. One volume per subject. The
class imbalance (roughly 3 : 2 : 1) and the small AD count (≈6 AD subjects per test
fold) are central to how we evaluate and how we interpret variance.


## 4. Method

### 4.1 Preprocessing

*3D pipeline.* Each volume is loaded, reoriented to a canonical axis order,
cropped to the nonzero brain bounding box, intensity-clipped to the 0.5–99.5
percentile range, z-scored over brain voxels, and resized to 128×128×128 for 3D CNNs
or 96×112×112 for the more memory-intensive 3D transformers.

*2.5D pipeline.* From each volume we extract, per anatomical plane (axial, coronal,
sagittal), 9 slices spanning 30–70% of the depth range, each resized to 224×224 —
27 slices per subject. This gives ImageNet-pretrained 2D backbones a multi-view
summary of the volume without the memory cost of full 3D.

All preprocessing statistics that could leak (none beyond per-volume normalization
here) are computed within each volume; tabular imputation and standardization
statistics are fit on the training fold only (Section 5).

### 4.2 TriFuse-AD architecture

TriFuse-AD has three parts:

**(1) Shared tri-planar CNN encoder.** All 27 slices (3 planes × 9 slices) pass
through a single ImageNet-pretrained ConvNeXt-Tiny backbone (num_classes=0, producing
a pooled feature vector per slice). A linear projection maps each to a common
embedding dimension. Sharing weights across planes and slices keeps the parameter
count small — important for a 198-subject cohort.

**(2) Slice-plane Transformer.** The 27 slice tokens receive additive slice-position
and plane embeddings (so the model can distinguish an axial slice at 40% depth from a
sagittal slice at 40% depth), a learned [CLS] token is prepended, and a 2-layer
Transformer encoder aggregates them. The [CLS] output is the MRI representation
`z_mri`, followed by layer normalization.

**(3) Gated demographic fusion.** A small MLP encodes the structured features into
`z_tab`. Fusion is gated:

```
g = σ(W_g · [z_mri ; z_tab])
z = LayerNorm(z_mri + g ⊙ (W_t · z_tab))
```

The gate `g` lets the model modulate how much demographic information is injected per
example, rather than blindly concatenating. A final 3-way linear head produces class
logits. The ablation (Section 7) replaces (2) with mean-pooling, replaces the gate
with plain concatenation, drops the metadata branch, and restricts to a single plane,
isolating each component's contribution.

### 4.3 Baselines

We compare against ten other models, all trained on identical folds:

- **Tabular:** XGBoost and an MLP on the structured features (age, sex, education,
  SES, eTIV, nWBV, ASF).
- **2D / 2.5D CNN:** ResNet50 (center axial slice), DenseNet121 (9-axial 2.5D).
- **3D CNN:** 3D ResNet18 (MONAI).
- **Transformers:** ViT-B/16 (2.5D, two-stage finetune), 3D Swin-T (MONAI).
- **Hybrids:** a compact 3D HCCT and a lightweight CNN-VSwinFormer, both retrained on
  our folds (no cross-dataset numbers copied).
- **Multimodal baseline:** DenseNet + late concatenation of structured features.


## 5. Experimental Setup

**Cross-validation.** We use subject-level repeated stratified 5-fold CV with seeds
{7, 13, 21} → **15 evaluations per model**. Stratification is by class × age band ×
sex. Splits are strictly subject-level; we assert zero subject overlap across
train / validation / test in every fold. All fold-dependent statistics
(tabular imputation medians, standardization, class-balance weights) are fit on the
training fold only.

**Training.** Torch models use AdamW with cosine schedule, two learning-rate groups
(a lower rate for pretrained backbones), bf16 automatic mixed precision, and early
stopping on validation Macro-F1. The default loss is class-balanced focal loss
(effective-number class weights, γ=2, label smoothing 0.05); a weighted
cross-entropy variant is used in the ablation. Hyperparameters are frozen per model
before the run; the test fold is never used for selection.

**Metrics.** The **primary metric is Macro-F1**, which weights all three stages
equally despite imbalance. We also report balanced accuracy, accuracy, macro
precision/recall, one-vs-rest macro AUC, and per-class F1/recall. We report
mean ± standard deviation over the 15 runs, and a bootstrap 95% CI on Macro-F1 from
the pooled out-of-fold (OOF) predictions. Significance between the proposed model and
the best baseline uses a paired permutation test on per-run Macro-F1.


## 6. Results

### 6.1 Main comparison

Table 1 reports Macro-F1 (mean ± std over 15 runs) with the bootstrap 95% CI, ordered
best to worst.

**Table 1 — Three-stage (CN/VMD/AD) classification, OASIS-1 age≥60, 198 subjects.**

| Rank | Model | Input | Macro-F1 | Bal. Acc. | F1(AD) | Macro-F1 95% CI |
|------|-------|-------|----------|-----------|--------|-----------------|
| 1 | DenseNet + concat | MRI+tab | **0.497 ± 0.062** | 0.525 | 0.390 | [0.47, 0.53] |
| 2 | **TriFuse-AD** | MRI+tab | 0.488 ± 0.066 | 0.508 | 0.367 | [0.46, 0.54] |
| 3 | XGBoost | tab | 0.474 ± 0.065 | 0.473 | 0.377 | [0.44, 0.52] |
| 4 | ViT-B/16 | 2.5D | 0.462 ± 0.085 | 0.490 | 0.323 | — |
| 5 | DenseNet121 | 2.5D | 0.449 ± 0.077 | 0.468 | 0.365 | — |
| 6 | 3D HCCT | 3D | 0.407 ± 0.078 | 0.450 | 0.279 | — |
| 7 | 3D Swin-T | 3D | 0.403 ± 0.085 | 0.442 | 0.280 | — |
| 8 | Tabular MLP | tab | 0.390 ± 0.097 | 0.442 | 0.412 | — |
| 9 | ResNet50 | 2D | 0.368 ± 0.074 | 0.399 | 0.205 | — |
| 10 | 3D ResNet18 | 3D | 0.354 ± 0.069 | 0.413 | 0.240 | — |
| 11 | CNN-VSwinFormer-lite | 3D | 0.354 ± 0.078 | 0.400 | 0.242 | — |

Three observations stand out.

**Multimodal fusion occupies the top two spots.** Both models that fuse MRI with
structured features (DenseNet+concat, TriFuse-AD) outrank every single-modality
model. Fusing morphometry/demographics with imaging is the single most reliable
lever on this cohort.

**But sophistication does not pay off.** TriFuse-AD's gated tri-planar attention
(0.488) does **not** beat the trivial DenseNet late-concatenation baseline (0.497).
A paired permutation test on per-run Macro-F1 gives Δ = −0.009, **p = 0.55** — the
two are statistically indistinguishable, and the point estimate actually favors the
simpler model. We therefore make no claim that TriFuse-AD is superior.

**No MRI-only network beats tabular XGBoost.** The best pure-imaging model (ViT-B/16,
0.462) sits below XGBoost on seven structured features (0.474), and the pure-3D
networks (3D ResNet18, CNN-VSwinFormer-lite, 3D Swin-T) are the weakest of all
(≈0.35–0.40) — below even the tabular MLP on some metrics. On a 198-subject cohort,
volumetric deep networks overfit and underperform a gradient-boosted table.

### 6.2 Variance and the small-AD problem

Standard deviations are large relative to the differences between models (±0.06 to
±0.10). The transformer models show the highest variance (ViT ±0.085, Swin ±0.085),
consistent with our prior expectation: with ≈6 AD subjects per test fold, a single
misclassified AD case swings fold-level Macro-F1 substantially. This is why we report
distributions over 15 runs rather than a single split, and why we treat rank
differences within a CI-width of each other as ties rather than wins.

Per-class F1 (Table 3b in the artifacts) confirms the AD class is the hardest
everywhere: F1(AD) tops out at 0.41 (tabular MLP, which trades CN/VMD precision for
AD recall) and is 0.37–0.39 for the fusion models. VMD (CDR=0.5) is the most
confused stage, frequently absorbed into CN or AD — expected, since it is the
clinical boundary zone.


## 7. Ablation Study

We ablate TriFuse-AD component-by-component, all variants sharing the full training
recipe and folds.

**Table 2 — TriFuse-AD ablation (Macro-F1, balanced accuracy, F1(AD)).**

| Variant | Macro-F1 | Bal. Acc. | F1(AD) |
|---------|----------|-----------|--------|
| A1: axial-only (single plane) | 0.477 ± 0.071 | 0.497 | 0.338 |
| A2: mean-pool (no Transformer) | 0.465 ± 0.083 | 0.497 | 0.367 |
| A3: no metadata (MRI only) | 0.390 ± 0.103 | 0.431 | 0.234 |
| A4: concat (no gate) | 0.487 ± 0.053 | 0.514 | 0.373 |
| A5: weighted-CE (vs focal) | **0.500 ± 0.054** | 0.518 | 0.413 |
| Full TriFuse-AD | 0.488 ± 0.066 | 0.508 | 0.367 |

The ablation is more informative than the headline number:

- **Metadata is the dominant component (A3).** Removing the demographic/morphometric
  branch collapses Macro-F1 from 0.488 to 0.390 and F1(AD) from 0.367 to 0.234 — a
  larger drop than any imaging-side change. This is the single clearest effect in the
  study and directly mirrors the confound analysis: the structured features carry
  most of the signal.

- **The gate does not help (A4).** Replacing gated fusion with plain concatenation
  gives 0.487 — identical to the full model (0.488) within noise, and with *lower*
  variance (±0.053 vs ±0.066). The gating mechanism, the architectural novelty of
  TriFuse-AD, earns nothing here.

- **The Transformer barely helps (A2).** Mean-pooling the slice tokens instead of the
  Transformer aggregator gives 0.465 vs 0.488 — a small, within-noise difference.

- **Tri-planar vs axial-only is marginal (A1).** Three planes (0.488) over axial-only
  (0.477) is again within noise.

- **Loss choice (A5).** Weighted cross-entropy slightly edges out class-balanced focal
  loss (0.500 vs 0.488) and gives the best F1(AD) of any TriFuse variant (0.413),
  though still within the confidence band.

**Takeaway:** the only component with a large, unambiguous effect is the metadata
branch. The imaging-side sophistication (tri-planar views, slice-plane Transformer,
gated fusion) contributes little beyond what a single-plane CNN with concatenated
demographics already achieves. This is an honest internal-validity result that we
report rather than bury.


## 8. Confound and Subgroup Analysis

### 8.1 How much signal is in the structured features alone?

To quantify confounding, we train shortcut baselines that use **no MRI at all**, on
the same folds/seeds and evaluated identically.

**Table 3 — No-MRI shortcut baselines (Macro-F1, balanced accuracy, AD recall).**

| Model | Features | Macro-F1 | Bal. Acc. | AD recall |
|-------|----------|----------|-----------|-----------|
| Age-only logistic | age | 0.324 ± 0.014 | 0.390 | 0.589 |
| Demographic XGBoost | age, sex, educ, SES | 0.417 ± 0.015 | 0.417 | 0.167 |
| Full-structured XGBoost | + eTIV, nWBV, ASF | 0.480 ± 0.014 | 0.476 | 0.367 |

The result is decisive. **A structured-feature model with no imaging reaches
Macro-F1 0.480** — higher than every MRI-only network in Table 1 and statistically
level with the best multimodal models (0.488–0.497). Adding the morphometric volumes
(eTIV, nWBV, ASF) to demographics lifts Macro-F1 from 0.417 to 0.480, i.e. brain
volume is doing most of the work. Age alone already recovers meaningful signal
(0.324, with high AD recall because AD subjects skew older even within the ≥60
cohort).

This is the crux of the paper. On OASIS-1 at age≥60, the recoverable three-stage
signal is largely **morphometric and demographic**, not something the deep networks
extract from raw voxels beyond what a single normalized-brain-volume number provides.
Any claim that a deep model "learns AD imaging biomarkers" here has to first clear
the 0.480 structured-only bar — and none of ours does so convincingly.

### 8.2 Subgroup robustness

Slicing the pooled OOF predictions by age band and sex (for the strongest models)
shows performance is **not stable across age**. For XGBoost, Macro-F1 falls from
0.735 in the 60–69 band to 0.401 in the 80+ band. The youngest band is easiest
because within age≥60 the 60–69 CN subjects still separate relatively cleanly; by
80+, atrophy is widespread across all three stages and the task is hardest. Sex
differences are smaller (male 0.486 vs female 0.440 for XGBoost). This age gradient
is itself evidence of residual confounding: even after restricting to ≥60, age
continues to structure the difficulty.


## 9. Interpretability

We apply Grad-CAM to TriFuse-AD's shared CNN encoder on a held-out fold (6 correctly
and 3 incorrectly classified test subjects), reporting the center slice per plane.
This is **qualitative only**. We do not claim the model localizes specific structures;
we observe that, for correctly classified AD/VMD cases, activation tends to fall over
peri-ventricular and medial-temporal regions — anatomy broadly associated with AD
atrophy — while misclassified cases show diffuse or off-target activation. Given the
confound analysis, we caution explicitly against over-reading these maps: a model
whose signal is dominated by whole-brain volume may attend to atrophy-correlated
regions incidentally rather than through disease-specific feature learning.


## 10. Discussion

**What we found.** Under a leakage-free, age-restricted, subject-level protocol on
OASIS-1: (i) multimodal fusion beats every single modality; (ii) but the proposed
gated tri-planar Transformer does not beat trivial late concatenation (p = 0.55),
and its ablation shows the metadata branch — not the imaging architecture — carries
the effect; (iii) no MRI-only network beats tabular XGBoost; and (iv) a no-MRI
structured model reaches Macro-F1 0.480, level with the best fusion models.

**Why this matters.** The dominant narrative in MRI-based AD staging is one of
steadily rising accuracy from ever-larger networks. Our controlled reproduction
suggests that on a small, realistic cohort much of the achievable signal is
morphometric/demographic and available without deep learning, and that architectural
novelty can vanish once trivial baselines and proper variance reporting are in place.
We think the field benefits more from this being stated plainly than from another
loosely-controlled record.

**Is TriFuse-AD useless?** No — but its value here is as a *well-specified, honestly
ablated* design point, not a winner. The gating and Transformer components are
defensible ideas that simply do not earn their complexity on 198 subjects; they may
behave differently on larger cohorts (ADNI-scale), which is the natural next test.

## 11. Limitations

- **Single cohort, small n.** 198 subjects, 30 AD; results are OASIS-1-specific and
  not validated cross-site. We make no cross-dataset or SOTA claims.
- **Non-clinical labels.** CDR-derived stages, with CDR=0.5 named VMD not MCI; not a
  clinical diagnosis. No diagnostic or clinical-decision claims.
- **Residual confounding.** Age≥60 attenuates but does not remove the age/atrophy
  confound, as the subgroup gradient shows.
- **Cross-sectional only.** No longitudinal progression modeling.
- **Compute environment.** The instance filesystem is non-persistent; all data and
  results are re-derivable from the released code, but no long-term artifact store is
  assumed.

## 12. Conclusion

We built a deliberately conservative three-stage dementia-staging benchmark on
OASIS-1 and evaluated eleven models plus a component ablation, confound analysis,
subgroup breakdown, and interpretability under one leakage-free protocol. Our
proposed TriFuse-AD is competitive but does **not** significantly outperform a
trivial multimodal baseline, and no imaging network beats a no-MRI structured model.
Rather than a state-of-the-art claim, we offer a reproducible, honestly-reported
reference point showing how much of the apparent MRI signal on small confounded
cohorts is actually morphometric — and a reminder that variance, confounding, and
trivial baselines must be reported before architectural credit is assigned.

---

*Reproducibility.* All models are trained on identical subject-level folds
(seeds 7/13/21 × 5-fold). Tables 1–3 and all figures (main comparison, per-model
confusion matrices, ROC curves, subgroup chart, Grad-CAM montage) are regenerated
deterministically from saved out-of-fold predictions. CDR and MMSE are excluded from
all model inputs.