One potential limitation of the multi-level supervision approach is the complexity of managing and integrating samples with different annotation levels. Ensuring that the network effectively learns from samples with varying levels of supervision can be challenging and may lead to issues such as information imbalance or conflicting signals. To address this limitation, future work could focus on developing more sophisticated training strategies, such as adaptive weighting of loss functions based on the annotation level of each sample. Additionally, incorporating techniques like self-supervised learning or semi-supervised learning could help in leveraging unlabeled data to improve the performance of the model.