A recurring task in MSI data analysis is to identify co-localized molecules, which is critical to the identification of key biochemical pathways of interest to biomarker discovery, drug development, and clinical diagnostics. Previous computational approaches used vector-based similarity measurements to determine molecular colocalizations. However, these methods cannot correlate highlevel spatial features making them disproportionately sensitive to the experimental artifacts and noise, which reduces their generalization capacity towards spatial patterns with similar localization but different contrast. Recently, transfer learning and semi-supervised deep learning approaches using convolutional neural network (CNN) have been developed to cluster ion images and quantify the molecular colocalization, respectively . These reports indicate that the limited size of MSI data presents a challenge to conventional CNN training frameworks, which typically rely on a large number of annotated images. As a result, these approaches provide a relatively minor improvement over the traditional machine learning methods for finding co-localized molecular images.