Pipeline: From MRI to Latent Signatures
The NeuroScope pipeline converts structural T1-weighted MRI and clinical variables into subject-level representations of neurodegenerative change. Our methodology is validated across OASIS-1 and ADNI-1 using standardized feature extraction to ensure cross-dataset compatibility.
Preprocessing & site-alignment
• Bias field correction & intensity normalization
• Skull stripping & template-space (MNI) alignment
• Resolution matching across multi-site datasets
• Handling label definition shifts (CDR vs. DX_bl)
ResNet18 + Anatomical
• 512D MRI embeddings via pre-trained ResNet18
• Regional brain volumes (Hippocampus, Ventricles)
• Global atrophy & Intracranial Volume (eTIV)
• Clinical covariates (Age, Sex, Education)
Late & Attention Fusion
• Modality-specific MLP encoders
• Late fusion (logit averaging/concatenation)
• Cross-modal attention mechanisms
• Explicit age modeling as a confounder
Ensuring publication-grade results
To ensure results are truly robust, we utilize the exact same architectures and hyperparameters for both in-dataset training and cross-dataset evaluation.
MRI-Only models serve as the baseline, with Multimodal (Late/Attention) fusion architectures evaluating the additive value of clinical signal in predicting early cognitive decline.
Beyond internal validation
Deep learning models often overfit to center-specific acquisition protocols. By testing our OASIS-trained models on ADNI (and vice-versa), we move beyond simple AUC reporting toward verifying the clinical stability of the learned brain signatures.
This rigorous validation approach is critical for assessing the feasibility of deploying these models in diverse real-world clinical settings.