Descriptive histopathology of mouse models of pneumonia is essential in assessing the outcome of infections, molecular manipulations or therapies in the context of whole lungs. Quantitative comparisons between experimental groups, however, have been limited to laborious stereology or ill-defined scoring systems that depend on the subjectivity of a more or less experienced observer. Here, we introduce self-learning digital image analyses that allow to transform optical information from whole mouse lung sections into statistically testable data. A pattern recognition-based software and a nuclear count algorithm were adopted to quantify user-defined pathologies from whole slide scans of lungs infected with Streptococcus pneumoniae or influenza A virus compared to PBS-challenged lungs. The readout parameters "relative area affected" and "nuclear counts per area" are proposed as relevant criteria for the quantification of lesions from hematoxylin-eosin stained sections, also allowing for the generation of a heat map of, e.g., immune cell infiltrates with anatomical assignments across entire lung sections. Moreover, when combined with immunohistochemical labelling of marker proteins both approaches are useful for the identification and counting of, e.g., immune cell populations, as validated here by direct comparisons with flow cytometry data. The solutions can easily and flexibly be adjusted to specificities of different models or pathogens. Automated digital analyses of whole mouse lung sections may set a new standard for the user-defined, high throughput comparative quantification of histological and immunohistochemical images. Still, our algorithms established here are only a start and need to be tested in additional studies and other applications in the future.