Transfer learning, while beneficial, has several limitations in medical image analysis:
Domain Shift: Pre-trained models are often trained on datasets that differ significantly from medical images (e.g., ImageNet). This domain shift can lead to poor performance when the model is applied to medical images. To address this, fine-tuning the model on a domain-specific dataset can help adapt the learned features to the new context.
Negative Transfer: As observed in the study, transfer learning can sometimes result in negative transfer, where the model's performance degrades. This can occur if the pre-trained model's features are not relevant to the target task. To mitigate this, careful selection of pre-trained models that are more aligned with the medical imaging domain is crucial.
Limited Data Availability: Medical datasets are often small and imbalanced, which can hinder the effectiveness of transfer learning. Techniques such as data augmentation, synthetic data generation, and semi-supervised learning can help increase the effective size of the training dataset.
Overfitting: Fine-tuning a pre-trained model on a small dataset can lead to overfitting. Implementing regularization techniques, such as dropout or weight decay, can help prevent overfitting and improve the model's generalization to unseen data.
Interpretability: Transfer learning models can be complex and difficult to interpret, which is a significant concern in medical applications. Employing explainable AI techniques, such as Grad-CAM or LIME, can help provide insights into the model's decision-making process, enhancing trust among clinicians.