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The integration of artificial intelligence into digital pathology has thepotential to automate and improve various tasks, such as image analysis anddiagnostic decision-making. Yet, the inherent variability of tissues, togetherwith the need for image labeling, lead to biased datasets that limit thegeneralizability of algorithms trained on them. One of the emerging solutionsfor this challenge is synthetic histological images. However, debiasing realdatasets require not only generating photorealistic images but also the abilityto control the features within them. A common approach is to use generativemethods that perform image translation between semantic masks that reflectprior knowledge of the tissue and a histological image. However, unlike otherimage domains, the complex structure of the tissue prevents a simple creationof histology semantic masks that are required as input to the image translationmodel, while semantic masks extracted from real images reduce the process'sscalability. In this work, we introduce a scalable generative model, coined asDEPAS, that captures tissue structure and generates high-resolution semanticmasks with state-of-the-art quality. We demonstrate the ability of DEPAS togenerate realistic semantic maps of tissue for three types of organs: skin,prostate, and lung. Moreover, we show that these masks can be processed using agenerative image translation model to produce photorealistic histology imagesof two types of cancer with two different types of staining techniques.Finally, we harness DEPAS to generate multi-label semantic masks that capturedifferent cell types distributions and use them to produce histological imageswith on-demand cellular features. Overall, our work provides a state-of-the-artsolution for the challenging task of generating synthetic histological imageswhile controlling their semantic information in a scalable way.

DEPAS: De-novo Pathology Semantic Masks using a Generative Model