As reported in a previous study, self-labeling improves the CNN model using a plain criterion: two independently augmented images from one ion should be classified into the same cluster. With this principle, the self-supervised training is able to enhance the generalization power of the model. In addition, only the confidently-classified ion images are included in this training 36 . As shown in Fig. and Fig. , we observed that the classification accuracy obtained for selected mouse uterine ion images increases with an increase in the softmax probability threshold. This indicates that the softmax probability threshold may be used to exclude falsely classified ion images from the training, which enhances the accuracy of the CNN model in the self-labeling step. In the implementation, we empirically selected the probability threshold with respect to the sample population. More specifically, after the initialization of the linear classifier, we selected the 40% quantile of softmax probabilities as the threshold. The selected training data containing 60% of the original ion images has a larger fraction of correctly classified images than the original data. Training samples were updated with the same probability threshold at every epoch, thus we gradually included more samples into the training (Fig. ). For each selected ion image, we applied a weak and a strong augmentations, respectively. Two pseudo labels were obtained after the encoder and classifier. A weighted cross-entropy loss was then applied to the minibatch of weakly augmented ion images to update the parameters of the CNN model.