Protein expression analysis is a crucial aspect of proteomics research, which is at the core of scientific discoveries daily, but techniques like Western Blotting require advanced technologies that are inaccessible by remote clinics. To tackle this problem, we investigated QuPath, an open-source software designed to analyze bioimages. Although the technology itself has already been developed, the application of QuPath to protein expression analysis thus far has not yielded a clear and reproducible workflow for those in remote or under-resources research environments. We applied this technology to the analysis of PARP-1 expression levels in immunohistologically-stained breast cancer slides. This included utilizing the cell detection and AI-based classification properties of QuPath. However, we realized that this process can be applied to any number of bioimages for a plethora of varying proteins. By feeding in human annotations from small segments of a slide, we discovered that QuPath's classification of cells allows for an easy quantification of protein expression levels for cells in different categories. Additionally, QuPath's automation capabilities make the process easy to run across a large number of slides after sufficient annotations are made. Further exploration of this method will serve to determine if there are improvements to be made in the realms of efficiency, accessibility, and accuracy in reporting proteomic profiles.