Optimizing Transformer-Based Models for Medical Image Analysis
Introduction Medical image analysis has significantly advanced with deep learning, particularly transformer-based models like Compact Convolutional Transformers (CCT) . These models offer improved feature extraction and attention mechanisms, making them effective for tasks like disease detection from radiological images . However, optimising these models requires careful tuning of hyperparameters, augmentation techniques, and regularization methods . In this blog, we will explore key strategies to optimize transformer-based models for medical image classification , ensuring higher accuracy and better generalization. 1. Data Preprocessing and Augmentation Medical datasets are often imbalanced and limited , making preprocessing essential. Here’s how you can improve model robustness: a) Handling Imbalanced Data Oversampling (e.g., SMOTE) or undersampling techniques to balance class distribution. Using weighted loss functions to give more importance to underrepresented classes. ...