We approached the challenge of molecular localization clustering as an image classification task. We aimed to re-train a CNN model for an individual MSI dataset to classify ion images based on the high-level spatial features without manual annotations. The model architecture is shown in Fig. . The pre-trained CNN (EfficientNet-B0) is re-trained by contrastive learning and self-labeling sequentially in a self-supervised manner. We use EfficientNet-B0, which has been trained on the ImageNet database. We used the EfficientNet-B0 model before the classification layer as an encoder. In our architecture, we firstly learned ion image representations through the contrastive learning. More specifically, SimCLR approach is adopted in this study. After this first phase of training, we fed ion images through the re-trained encoder to produce a set of feature vectors, which were then passed to a spectral clustering (SC) classifier to generate the initial labels for the classification task. To initialize self-labeling, a linear classifier (a linear layer followed by a softmax function) was attached to the encoder and trained with the original ion images and initial labels as inputs. Finally, we utilized a self-labeling approach to fine-tune both the encoder and classifier, which allows the network to correct itself.