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The development of high-plex spatial profiling approaches able to measure protein and RNA expression at single-cell resolution augments but does not replace H&amp;E imaging, even in research. Computational pathology (CPath) models based on deep learning promise to further increase the value of H&amp;E imaging but interpreting these models in biological terms remains challenging. As a result, they are not widely used in spatial profiling studies. Here we describe a human-in-the-loop computational framework that leverages CPath foundation models (FMs) and sparse autoencoders (SAEs) to decompose FM embeddings and automatically identify diverse, human-interpretable histopathology features in H&amp;E images. When FM-SAE modeling was applied to pulmonary diseases such as tuberculosis and lung cancer, human-machine interaction augmented and accelerated expert interpretation. Moreover, the resulting annotations provide a morphology-aware approach to integrating 2D and 3D mesoscale tissue architectures with molecular spatial profiling.<\/jats:p>","DOI":"10.64898\/2026.06.03.725182","type":"posted-content","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T19:05:13Z","timestamp":1780772713000},"source":"Crossref","is-referenced-by-count":2,"title":["Compositional and interpretable representation of histology using AI foundation models and sparse autoencoders"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2019-1406","authenticated-orcid":false,"given":"Ziyuan","family":"Zhao","sequence":"first","affiliation":[{"name":"Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA"},{"name":"Ludwig Centre at Harvard, Harvard Medical School, Boston, MA, USA"},{"name":"Department of Systems Biology, Harvard Medical School, Boston, MA, 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