tensor-network-neurological-predictor

# Tensor Network Feature Engineering for Neurological Disorder Prediction Multi-class neurological disorder prediction using tensor network feature engineering from sparse MRI imaging data. ## Core Concept MRI scans for neurological disorders often use sparse imaging techniques to reduce scan time. Tensor network methods can extract rich features from these sparse representations, enabling accurate multi-class disorder classification. ## Architecture ### Tensor Representation ```python import numpy as np from tensorly.decomposition import tucker, cp import tensorly as tl def build_mri_tensor(mri_slices, num_slices=64): """Build 3D tensor from MRI slice data. Args: mri_slices: List of 2D MRI slices num_slices: Target number of slices Returns: tensor: 3D tensor (height, width, depth) """ h, w = mri_slices[0].shape tensor = np.zeros((h, w, num_slices)) for i, slice_data in enumerate(mri_slices[:num_slices]): tensor[:, :, i] = slice_data return tensor ``` ### Tensor Network Decomposition ```python def extract_tensor_features(tensor, rank=(16, 16, 8)): """Extract features using Tucker decomposition. Args: tensor: 3D MRI tensor rank: Target ranks for each mode Returns: features: Flattened feature vector from core tensor """ tensor_tl = tl.tensor(tensor) # Tucker decomposition core, factors = tucker(tensor_tl, rank=rank) # Flatten core tensor as features features = tl.to_numpy(core).flatten() return features ``` ### Multi-Class Classification Pipeline ```python from sklearn.svm import SVC from sklearn.model_selection import cross_val_score def classify_disorders(features, labels, n_classes=3): """Classify neurological disorders using tensor features. Args: features: (n_samples, n_features) tensor features labels: Disorder class labels n_classes: Number of disorder classes Returns: accuracy: Cross-validation accuracy model: Trained classifier """ model = SVC(kernel='rbf', decision_function_shape='ovo') scores = cross_val_score(model, features, labels, cv=5) model.fit(features, labels) return scores.mean(), model ``` ## Workflow 1. **Data Preprocessing**: - Load sparse MRI data - Normalize intensity values - Handle missing slices via interpolation 2. **Tensor Construction**: - Stack 2D slices into 3D tensor - Apply spatial normalization if needed - Handle varying resolutions 3. **Feature Extraction**: - Apply Tucker/CP decomposition - Extract core tensor features - Optionally add handcrafted features 4. **Classification**: - Train multi-class classifier - Use cross-validation for evaluation - Handle class imbalance 5. **Interpretation**: - Analyze factor matrices for brain regions - Map important features to anatomical locations ## Parameters - **Tucker Rank**: (16, 16, 8) for typical MRI resolution - **Classifier**: SVM with RBF kernel or Random Forest - **Cross-validation**: 5-fold or leave-one-out - **Preprocessing**: Intensity normalization, skull stripping ## Advantages - **Handles Sparsity**: Works well with reduced MRI acquisition - **Captures 3D Structure**: Preserves spatial relationships - **Multi-Class**: Supports multiple disorder types simultaneously - **Interpretable**: Factor matrices reveal important brain regions ## Use Cases - Alzheimer's disease detection - Parkinson's disease classification - Multiple sclerosis identification - Brain tumor classification - Multi-disorder differential diagnosis ## References - Balakrishna et al. (2026). "Multi-Class Neurological Disorder Prediction with Tensor Network Feature Engineering" (arXiv:2605.17771) ## Related Skills - quantum-ml-healthcare - medical-ai-diagnosis - tensor-network-medical-imaging