tensor-network-medical-imaging - SKILL.md Agent Skill

name: tensor-network-medical-imaging description: Quantum-inspired tensor network feature engineering for medical image classification. Use PARAFAC/CP tensor decompositions to extract discriminative features from medical imaging data (MRI, CT, X-ray) for multi-class neurological disorder prediction and clinical diagnosis. Applicable to any high-dimensional medical imaging classification task where tensor decompositions can capture latent structure.

Tensor Network Medical Imaging

Description

Apply quantum-inspired tensor network decompositions (PARAFAC/CP) to medical imaging data for multi-class classification. This approach converts high-dimensional images into compact tensor representations, decomposes them into latent factors, and feeds these features into ensemble classifiers for robust neurological disorder prediction.

Activation Keywords

tensor network medical
PARAFAC medical imaging
quantum-inspired classification
neurological disorder prediction
tensor decomposition MRI
CP decomposition medical
medical image tensor features
tensor feature engineering
PARAFAC CP decomposition
张量网络医学成像
医疗图像分类

Tools Used

python: Execute tensor decomposition and classification scripts
sklearn: Classification models and cross-validation
tensorly: Tensor operations and PARAFAC decomposition

Installation

pip install tensorly scikit-learn numpy pandas

Usage Patterns

Pattern 1: MRI-based Neurological Disorder Classification

from tensorly.decomposition import parafac
from sklearn.ensemble import RandomForestClassifier
import numpy as np

# Load medical imaging data (N_samples, height, width, channels)
images = load_medical_images()  # Shape: (N, H, W, C)

# Convert to tensor format for decomposition
tensor_data = images.reshape(-1, H, W, C)

# Apply PARAFAC decomposition
weights, factors = parafac(tensor_data, rank=64, n_iter_max=100)

# Use factors as features for classification
features = factors[0]  # Component weights
labels = get_diagnosis_labels()

# Train classifier with nested cross-validation
from sklearn.model_selection import cross_val_score
clf = RandomForestClassifier(n_estimators=100)
scores = cross_val_score(clf, features, labels, cv=5)

Pattern 2: Multi-Modal Medical Image Fusion

# Combine multiple imaging modalities using tensor fusion
mri_tensor = load_mri_data()
ct_tensor = load_ct_data()

# Stack modalities as additional tensor mode
fused_tensor = np.stack([mri_tensor, ct_tensor], axis=-1)

# Decompose fused tensor
weights, factors = parafac(fused_tensor, rank=128)

# Use decomposed features for enhanced classification

Instructions for Agents

Step 1: Data Preparation

Load medical imaging data into numpy arrays
Ensure consistent preprocessing (normalization, resizing)
Handle missing modalities gracefully
Balance dataset across diagnostic categories

Step 2: Tensor Decomposition

Choose appropriate PARAFAC rank (start with 32-128)
Apply decomposition with sufficient iterations (100+)
Validate decomposition quality with explained variance
Extract component weights and factor matrices

Step 3: Feature Engineering

Use component weights as primary features
Consider factor matrices for spatial/structural features
Combine with traditional features if beneficial
Handle high-rank vs low-rank configurations

Step 4: Classification

Use ensemble methods (Random Forest, Gradient Boosting)
Implement nested stratified cross-validation
Report both accuracy and per-class metrics
Compare with baseline approaches

Error Handling

Tensor Decomposition Fails

If decomposition doesn't converge:
  1. Increase n_iter_max (try 200-500)
  2. Reduce rank
  3. Check for NaN/Inf in input data
  4. Try different initialization

Memory Issues

For large datasets:
  1. Use mini-batch processing
  2. Reduce spatial resolution temporarily
  3. Apply PCA before tensor decomposition
  4. Use sparse tensor representations

Best Practices

Rank Selection: Start with lower ranks (32-64) for robustness, increase if needed
Cross-Validation: Always use nested stratified CV for reliable estimates
Baseline Comparison: Compare against PCA, autoencoders, and CNNs
Interpretability: Factor matrices can reveal spatial patterns relevant to diagnosis
Multi-Modal: Tensor decomposition naturally handles multiple imaging modalities

Examples

Example: 8-Class Neurological Disorder Prediction

# Based on arXiv:2605.17771
# Dataset: 55,160 MRI images across 8 diagnostic categories

import tensorly as tl
from tensorly.decomposition import parafac
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import StratifiedKFold
import numpy as np

# Load data
images = np.load('mri_dataset.npy')  # (55160, 128, 128, 3)
labels = np.load('diagnosis_labels.npy')  # 8 classes

# PARAFAC decomposition
tensor_images = images.reshape(-1, 128, 128, 3)
weights, factors = parafac(tensor_images, rank=64, n_iter_max=100)

# Classification with 5-fold CV
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
clf = RandomForestClassifier(n_estimators=100, random_state=42)

for train_idx, test_idx in skf.split(weights, labels):
    clf.fit(weights[train_idx], labels[train_idx])
    accuracy = clf.score(weights[test_idx], labels[test_idx])
    print(f"Fold accuracy: {accuracy:.4f}")

Related Skills

quantum-medical-diagnosis: Quantum computing for medical diagnosis
medical-ai-diagnosis: AI-based medical diagnosis patterns
brain-network-controllability: Brain network analysis

Resources

arXiv:2605.17771 - Original paper on PARAFAC for neurological disorder prediction
TensorLy documentation: https://tensorly.org/
scikit-learn ensemble methods: https://scikit-learn.org/