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

翻译：将人工智能整合到数字病理学中，有望实现图像分析和诊断决策等任务的自动化与改进。然而，组织固有的变异性以及图像标注的需求导致了有偏数据集，限制了在此类数据上训练的算法的泛化能力。针对这一挑战，新兴解决方案之一是合成组织学图像。但消除真实数据集偏差不仅需要生成逼真图像，还需具备控制图像内特征的能力。常用方法是采用生成式方法，在反映组织先验知识的语义掩膜与组织学图像之间进行图像转换。然而，与其他图像领域不同，组织复杂结构使得无法简易创建图像转换模型所需的组织学语义掩膜作为输入，而从真实图像中提取语义掩膜则降低了流程的可扩展性。本文提出了一种名为DEPAS的可扩展生成模型，该模型能捕获组织结构，并生成具有最先进质量的高分辨率语义掩膜。我们展示了DEPAS为皮肤、前列腺和肺三种器官生成逼真组织语义图的能力。此外，我们证明这些掩膜可使用生成式图像转换模型进行处理，生成两种癌症类型在两种不同染色技术下的逼真组织学图像。最后，我们利用DEPAS生成捕获不同细胞类型分布的多标签语义掩膜，并以此生成具有按需细胞特征的组织学图像。总体而言，我们的工作为以可扩展方式控制语义信息生成合成组织学图像这一挑战性任务提供了最先进的解决方案。