One potential limitation of the Multiple Instance Learning (MIL) strategy used in the method is the sensitivity to noisy or mislabeled instances within the patches. To address this limitation, several approaches can be considered:
Instance Selection: Implement a mechanism to identify and filter out noisy instances during training. This can involve incorporating instance weighting or sampling techniques to downweight the influence of noisy instances on the training process.
Instance Confidence: Introduce a confidence measure for each instance prediction to prioritize more confident predictions during the MIL aggregation process. This can help reduce the impact of uncertain or incorrect predictions on the final decision.
Regularization Techniques: Apply regularization techniques such as dropout or batch normalization to prevent overfitting to noisy instances and improve the model's robustness to label noise.
Ensemble Methods: Utilize ensemble methods to combine predictions from multiple models trained with different subsets of the data. Ensemble learning can help mitigate the effects of noisy instances and improve the overall performance of the model.