What Are The Optimal Hyperparameters For A Deep Learning-based Computer Vision Model To Detect And Quantify Subtle Differences In Eye Movement Patterns And Facial Expressions In Children With Autism Spectrum Disorder, Specifically Using A Dataset Comprising 3D Facial Landmark Tracking And Gaze Estimation From Wearable Eye-tracking Devices During Social Interaction Tasks?

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To determine the optimal hyperparameters for a deep learning model aimed at detecting and quantifying subtle differences in eye movement patterns and facial expressions in children with autism spectrum disorder (ASD), consider the following structured approach:

Model Architecture

  1. Two-Stream Network:

    • Stream 1 (Facial Landmarks): Utilize a 3D CNN (e.g., ResNet3D) to capture spatial and temporal features from 3D facial landmark data.
    • Stream 2 (Gaze Data): Employ a 3D CNN or a combination of CNN and RNN to process gaze estimation data, considering its temporal aspects.

  2. Temporal Processing:

    • Integrate LSTMs or GRUs, particularly bidirectional LSTMs, to model long-term dependencies in eye movements and expressions.

  3. Attention Mechanisms:

    • Incorporate self-attention layers post-CNN and RNN to focus on relevant features, enhancing the model's ability to capture critical patterns.

Hyperparameters

  1. Learning Rate:

    • Start with a lower learning rate (e.g., 1e-4) and implement a scheduler to adjust during training.

  2. Batch Size:

    • Use the largest possible batch size given memory constraints, typically between 16 to 64.

  3. Epochs and Early Stopping:

    • Train for 50-100 epochs with early stopping patience of 10-15 epochs to prevent overfitting.

  4. Optimizer:

    • Use Adam optimizer with weight decay (0.0001) for regularization.

  5. Loss Function:

    • Cross-entropy for classification tasks; MSE or MAE for regression.

  6. Regularization:

    • Apply dropout (0.2-0.5) and weight decay to prevent overfitting.

  7. Data Augmentation:

    • Augment data with noise, rotations, and synthetic examples to enhance generalization.

  8. Fusion Strategy:

    • Fuse facial and gaze data at the feature level to leverage interactions between modalities.

  9. Class Balancing:

    • Employ techniques like class weighting or oversampling to address imbalanced datasets.

  10. Model Complexity:

    • Start with a simpler architecture, gradually increasing complexity as needed.

Evaluation and Validation

  1. Metrics:

    • Use accuracy, AUC-ROC, precision, recall, and F1-score for comprehensive evaluation.

  2. Cross-Validation:

    • Implement k-fold cross-validation to ensure consistent performance across data splits.

  3. Interpretability:

    • Utilize saliency maps or SHAP values to understand model decisions, crucial for clinical validation.

Computational and Practical Considerations

  • Preprocessing: Normalize and align facial data to standard positions.
  • Transfer Learning: Leverage pre-trained models and fine-tune on the target dataset.
  • Resource Management: Opt for efficient architectures if computational resources are limited.

Hyperparameter Tuning

  • Conduct grid search or random search with cross-validation, or use Bayesian optimization for efficient tuning.

By systematically addressing each component, the model can effectively detect and quantify subtle differences, providing valuable insights for ASD diagnosis and research.