组织病理学图像分割是一项费力且耗时的任务，通常需要经验丰富的病理学家进行分析才能进行准确的检查。为了减轻这种负担，已采用监督机器学习方法，使用大规模注释数据集进行组织病理学图像分析。然而，在某些情况下，大规模注释数据的可用性是训练此类模型的瓶颈。自监督学习（SSL）是一种替代范式，它通过仅利用通常丰富的未注释数据构建模型来提供一些喘息的机会。 SSL 的基本思想是训练网络对未注释的数据执行一项或多项伪或借口任务，并随后将其用作各种下游任务的基础。可以看出，SSL 的成功关键取决于所考虑的借口任务。虽然人们在设计分类问题的借口任务方面做出了很多努力，但在 SSL 上进行组织病理学图像分割的尝试却很少。受此启发，我们提出了一种 SSL 方法，通过生成扩散模型分割组织病理学图像。我们的方法基于这样的观察：扩散模型有效地解决了类似于分割任务的图像到图像的翻译任务。因此，我们提出生成扩散作为组织病理学图像分割的借口任务。我们还利用基于多重损失函数的微调来进行下游任务。我们使用两个公开可用数据集的多个指标以及新提出的包含苏木精和曙红（HN）的头颈（HN）癌症数据集来验证我们的方法

Histopathological image segmentation is a laborious and time-intensive task, often requiring analysis from experienced pathologists for accurate examinations. To reduce this burden, supervised machine-learning approaches have been adopted using large-scale annotated datasets for histopathological image analysis. However, in several scenarios, the availability of large-scale annotated data is a bottleneck while training such models. Self-supervised learning (SSL) is an alternative paradigm that provides some respite by constructing models utilizing only the unannotated data which is often abundant. The basic idea of SSL is to train a network to perform one or many pseudo or pretext tasks on unannotated data and use it subsequently as the basis for a variety of downstream tasks. It is seen that the success of SSL depends critically on the considered pretext task. While there have been many efforts in designing pretext tasks for classification problems, there have not been many attempts on SSL for histopathological image segmentation. Motivated by this, we propose an SSL approach for segmenting histopathological images via generative diffusion models. Our method is based on the observation that diffusion models effectively solve an image-to-image translation task akin to a segmentation task. Hence, we propose generative diffusion as the pretext task for histopathological image segmentation. We also utilize a multi-loss function-based fine-tuning for the downstream task. We validate our method using several metrics on two publicly available datasets along with a newly proposed head and neck (HN) cancer dataset containing Hematoxylin and Eosin (H&E) stained images along with annotations.