To enhance the generalization capabilities of the proposed deep learning models for spinal cord injury (SCI) diagnostics, several strategies can be employed:
Diverse Training Data: Expanding the dataset to include a wider variety of patient demographics, imaging conditions, and anatomical variations is crucial. This could involve collecting ultrasound images from different populations, including varying age groups, genders, and comorbidities, as well as images captured under different clinical settings and equipment. Such diversity can help the models learn to recognize a broader range of anatomical features and pathological conditions.
Transfer Learning: Utilizing transfer learning techniques can significantly improve model performance on unseen data. By pre-training models on large, diverse medical imaging datasets (e.g., Synapse multi-organ dataset), the models can learn foundational features that are applicable across different imaging modalities and conditions. Fine-tuning these models on the specific porcine and human spinal cord datasets can help adapt them to the nuances of ultrasound imaging.
Data Augmentation: Implementing advanced data augmentation techniques tailored for ultrasound images can enhance model robustness. Techniques such as physics-inspired transformations (e.g., simulating noise, reverb, and varying signal-to-noise ratios) can create synthetic variations of the training data, helping the models to generalize better to real-world imaging conditions.
Domain Adaptation: Employing domain adaptation methods can bridge the gap between training and testing domains. Techniques such as canonical correlation analysis or optimal transport can be used to align the feature distributions of porcine and human datasets, improving the model's ability to generalize from one domain to another.
Ensemble Learning: Combining predictions from multiple models can enhance overall performance and robustness. By leveraging the strengths of different architectures (e.g., YOLOv8 for injury localization and DeepLabv3 for segmentation), ensemble methods can provide more accurate and reliable predictions across diverse imaging scenarios.
Continuous Learning: Implementing a continuous learning framework where models are periodically updated with new data can help maintain their relevance and accuracy over time. This approach allows the models to adapt to evolving imaging techniques and patient populations, ensuring sustained performance.