tensor-network-neurological-predictor

name	tensor-network-neurological-predictor
description	Tensor Network Feature Engineering methodology for multi-class neurological disorder prediction from MRI data. Uses tensor network decompositions to extract high-dimensional features from sparse medical imaging. Activation: tensor network MRI, neurological disorder prediction, tensor feature engineering, multi-class brain disorder, MRI tensor decomposition.

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.

Source: arXiv:2605.17771 (May 2026) — Balakrishna et al. "Multi-Class Neurological Disorder Prediction with Tensor Network Feature Engineering". Evaluated on 55,160 images across 8 diagnostic categories using 5-fold nested stratified cross-validation. Both higher and lower PARAFAC rank configurations achieve strong performance, demonstrating robustness to tensor network expressivity.

PARAFAC CP Decomposition Approach

The paper uses PARAFAC CP (Canonical Polyadic) tensor decomposition, which is mathematically inspired by quantum neural network architectures but implemented entirely classically:

Architecture

Tensor Representation

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

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

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