The pseudo-labeling approach, while effective in leveraging unannotated data, has several potential limitations. One significant concern is the introduction of noise from incorrect pseudo-labels, which can mislead the training process and degrade model performance. This issue is particularly pronounced in medical imaging, where the consequences of misclassification can be severe.
To address this limitation, a more robust confidence thresholding mechanism can be implemented. Instead of using a fixed threshold, a dynamic thresholding approach could be employed, where the threshold is adjusted based on the model's performance on a validation set. This would allow for more adaptive filtering of pseudo-labels, ensuring that only the most reliable predictions are included in the training process.
Another strategy is to incorporate ensemble methods, where multiple models generate pseudo-labels, and only the labels that are consistently predicted across models are retained. This can help mitigate the impact of individual model biases and improve the overall quality of the pseudo-labels.
Additionally, implementing a feedback loop where the model's predictions are periodically validated against a small set of expert annotations can help refine the pseudo-labeling process. By continuously updating the model based on expert feedback, the framework can learn to correct its predictions over time, enhancing robustness.