## 4 Discussion  
Our experimental results demonstrate that the proposed methods effectively deal with the imbalanced learning challenge, in which the model is absolutely biased towards the majority class. The methods are not limited by the network used, and can be applied to different backbone networks as well. The results would be improved if a larger-scale unlabeled dataset could be collected and used without being limited by the computational power provided. Nonetheless, the feasibility, effectiveness, and generality of the proposed methods are indicated experimentally. The improved performance of the proposed methods in DR detection can be attributed to the approach of utilizing unlabeled data from the same domain, and to the specific strategies of dealing with the imbalanced learning issue in the case of training a model using an imbalanced dataset. The experimental results demonstrate that the sensitivity of the model can be increased substantially in each of these approaches using the wrapped self-supervised or semi-supervised algorithm. This means that a model with classification bias caused by imbalanced data distribution can be re-balanced to give more attention to the minority class (samples with DR disease), thus, the performance can be improved. Moreover, another improvement was observed when combining self-supervised learning with semi-supervised learning; this approach led to a significant reduction in training time. The effectiveness of this approach is obvious, as the model initially trained with supervised learning on labeled data certainly did not perform as well as the model that had been fine-tuned with the help of the pre-trained model, which was trained using self-supervised learning. However, self-training using semi-supervised learning is relatively inefficient due to the iterative process. The semi-supervised learning method still suffers from the imbalanced dataset problem, which still poses a major challenge for semi-supervised learning, the current practice of which is to use a fully balanced dataset with reference to their pseudo labels. This is only feasible in the case where pseudo labels can be generated; what if this is not possible in practice? In this instance, it is feasible to use self-supervised learning to train the model first. Therefore, the research of combining semi-supervised and self-supervised learning is also a novel direction. It is difficult for self-supervised learning to interpret how to learn the underlying representation. Nonetheless, because of its explicability, self-supervised learning is said to be the closest AI principle to the way humans see. It can be seen from the two experimental results for the imbalanced and balanced datasets that the performance of the model trained on the relatively smaller balanced dataset was not worse than that of the counterpart, which was the relatively larger imbalanced dataset. Furthermore, when using the small balanced dataset, there is no need to employ the additional re-balancing strategy of fine-tuning the classifier; it also saved computational costs because of its shorter training process. Thus, it raises the following question: why do we use large imbalanced datasets if there are appropriate ways to make the model perform as effectively on smaller datasets as it does on larger ones? This indicates that deep-learning-based CNN models do not necessarily have to use large datasets to achieve better performance. It is crucial to use a small amount of data to make the model perform well under the extraordinary difficulty of collecting clean and labeled medical data. If a model achieves good results on a small dataset, then the model will be much less dependent on a large amount of labeled data.