It has been demonstrated in a previous report 36 and this study (Fig. ) that images with similar high-level spatial features are mapped together in the representation space using contrastive learning. To leverage the meaningful local neighborhoods, SC was adopted to cluster ion images as the classification pretext task. Based on the cosine distance, 10 nearest neighbors of each ion image were identified to construct a graph. Next, we used the discretization approach to cluster nodes in the graph after the Laplacian embeddings 35 . After the SC, we used the resulting classification labels to initialize a learnable linear classifier on top of the CNN encoder, which enables the following self-labeling process to fine-tune the model (step 2 in Fig. ). More specifically, the linear classifier is composed of a linear layer and a softmax function. We set the encoder in frozen mode and trained the classifier with original ion images and initial labels obtained from SC.