Because the initial labels are generated using an unsupervised machine learning approach, we anticipate that some false classification may be present. We identified falsely classified images based on their softmax probabilities . By excluding these images from training, we improved the robustness of the CNN model, which benefits the classification accuracy. In order to select images with correct classification during the training, we first examined the relationship between the softmax probability and classification accuracy for the CNN model using the benchmark dataset. This model was trained by initial labels obtained from SC and classified ion images into 13 classes. We used a range of softmax probability thresholds to examine the classification accuracy (red trace) and fraction of confidently identified images (blue trace) as shown in Fig. . We observe that the classification accuracy increases with increase in the softmax probability threshold. Meanwhile, the number of confidently classified images decreases. Additional examples of this analysis are shown in Fig. indicating that the observed trend is general.