We used the self-supervised clustering approach to cluster benchmark ion images of the mouse uterine tissue (Fig. ) into 13 and 20 groups. The results obtained at different stages of the workflow for five replicates are summarized in Table . When clustering is performed using the CNN encoder and SC, contrastive learning (SimCLR) improves the classification accuracy from 64.8% to 81.5% with 13 clusters and from 71.9% to 90.0% with 20 clusters. An improvement of about 20% in accuracy clearly indicates the significance of the CNN retraining for learning image representations in MSI data. In addition, self-labeling provides a 3% improvement in the classification accuracy for both 13 and 20 clusters. Collectively, our self-supervised clustering approach enabled clustering of the benchmark data into 20 groups with 92.7% accuracy as shown in Fig. . Representative ion images for each group shown in Fig. provide a concise summary of the spatial patterns present in the vast MSI data. Meanwhile, the generalization power of the self-supervised clustering approach and its tolerance to noise levels can be assessed by examining images in each class (Fig. ).