Delta Marches to autonomously learn histopathology rules by generative latent space traversals

Deep learning (DL) has excelled in tissue image classification, presenting opportunities to discover biological behaviors escaping visual inspection. However, the methods to produce fine-grained insights from spatially scattered information do not exist. Here, we introduce Delta-Marches, a framework for mechanistic interpretability that leverages generative models to produce high-fidelity images from semantic latent representations. By identifying directions in this space corresponding to class transitions, we simulate controlled morphological changes between classes. Comparing each image to its class-shifted counterpart enables a secondary model to nominate features most affected by the shift. This approach overcomes sample-to-sample variability and yields idealized, interpretable transformations at subcellular resolution. We prototype the approach in the context of histopathological grading of clear cell renal cell carcinoma. Delta-Marches generate synthetic grade transitions indistinguishable from real images and autonomously pinpoints nuclear enlargement and increased nucleolar count in tumor cells as key properties of higher grades - features identifiable only through the method's subcellular precision. In addition to these features mirroring clinical criteria, it also reveals reduced vasculature, a pattern reported in multiple studies but absent from standard grading rubrics. These results indicate Delta-March's potential to convert complex and spatially-distributed features into rules for image classification.