Microscopic images of human tissue are a cornerstone of biomedical research and clinical diagnostics. Yet despite their importance, these images often remain difficult to analyze systematically and to connect with other types of biological data. A new study led by CeMM Principal Investigator André Rendeiro and published in Nature Methods introduces “LazySlide,” an open-source software tool that brings the power of foundation models and aims to democratize digital pathology analysis.
Whether it’s an inflamed artery, a tumor spreading into the lung or subtle damage in an organ, when doctors or researchers want to understand what’s happening inside a tissue, one of the most trusted tools is still the microscope. Today, they have largely gone digital: A single tissue sample can be scanned into a whole-slide image so detailed that one can zoom from a bird’s-eye view of the entire tissue down to individual cells. These images, therefore, contain enormous information about tissues from different scales.
However, these images are huge, complex, and often difficult to analyze in a modern, data-driven way. While genetics and single-cell biology have developed effective ways for sharing and comparing data, digital pathology images are hard to incorporate—stored in proprietary formats, processed with incompatible tools, and hard to connect to molecular information like RNA sequencing. Thus, the valuable resources of digitalized tissue images are largely underutilized in many research and clinical settings.
