The dataset viewer is not available for this dataset.
Error code: JWTInvalidSignature
Exception: InvalidSignatureError
Message: Signature verification failed
Traceback: Traceback (most recent call last):
File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
decoded = jwt.decode(
jwt=token,
...<2 lines>...
options=options,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
decoded = self.decode_complete(
jwt,
...<8 lines>...
leeway=leeway,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
decoded = self._jws.decode_complete(
jwt,
...<3 lines>...
detached_payload=detached_payload,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
self._verify_signature(
~~~~~~~~~~~~~~~~~~~~~~^
signing_input,
^^^^^^^^^^^^^^
...<4 lines>...
options=merged_options,
^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
raise InvalidSignatureError("Signature verification failed")
jwt.exceptions.InvalidSignatureError: Signature verification failedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
TriFuse-AD: Honest Multimodal Benchmark for Three-Stage Dementia Staging on OASIS-1
Code, processed data, results, and paper for a leakage-free benchmark of three-stage cognitive classification (CN / VMD / AD) on the OASIS-1 cross-sectional cohort, plus the proposed TriFuse-AD model (tri-planar CNN + slice-plane Transformer + gated demographic fusion).
Key result (honest / negative)
On an age-restricted cohort (≥60, 198 subjects) with subject-level repeated 5-fold CV (3 seeds, 15 runs/model), no MRI-only network beats a plain tabular XGBoost (Macro-F1 0.474), and TriFuse-AD (0.488 ± 0.066) does not significantly beat a trivial DenseNet late-concat baseline (0.497 ± 0.062; paired permutation p = 0.55). A no-MRI structured model reaches Macro-F1 0.480 — most recoverable signal is morphometric/demographic, not learned from raw voxels. No clinical / diagnostic / SOTA / MCI / cross-site claims.
Repository layout
| Path | Contents |
|---|---|
src/ |
trifuse package: data, models, training, eval, analysis |
scripts/ |
experiment runner, table/figure/interpretability builders |
configs/ |
model configs |
results/ |
per-model OOF preds, summaries, tables, 27 figures |
paper/trifuse_ad.md |
full paper draft |
data_processed.zip |
preprocessed 2.5D + 3D arrays + subjects_clean.csv (1.5 GB) |
data/raw/*.tar.gz |
OASIS-1 cross-sectional discs 1–12 (16 GB) |
Reproducing
pip install -r requirements # torch cu128, timm, monai, nibabel, xgboost, sklearn, ...
unzip data_processed.zip # -> data/processed_2d, processed_3d, metadata
python scripts/run_experiments.py --grid main # 11 models x 15 runs
python scripts/run_experiments.py --grid ablation # 6 variants
python scripts/make_tables.py && python scripts/make_figures.py
Cohort
OASIS-1, age≥60 → 198 subjects (CN=98, VMD=70, AD=30). Labels from CDR (0→CN,
0.5→VMD, ≥1→AD). CDR and MMSE are never model inputs (label leakage). One volume
per subject (*_111_t88_masked_gfc).
License / data use
The data/raw/ tarballs are the original OASIS-1 cross-sectional release
(Marcus et al., 2007), redistributed here for reproducibility. OASIS data are subject
to the OASIS data-use terms; if you use them, cite the OASIS project and comply with
their agreement. Code and derived results in this repo are provided for research use.
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