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 haven't
been many attempts on SSL for histopathological segmentation. Motivated by
this, we propose an SSL approach for segmenting histopathological images via
generative diffusion models in this paper. 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
propose a multi-loss function-based fine-tuning for the downstream task. We
validate our method using several metrics on two publically available datasets
along with a newly proposed head and neck (HN) cancer dataset containing
hematoxylin and eosin (H\&E) stained images along with annotations. Codes will
be made public at
this https URL

通过利用生成扩散模型，我们提出了一种自监督学习方法，用于分割组织病理学图像，并采用基于多损失函数的微调方法进行下游任务。

GenSelfDiff-HIS: 使用扩散进行组织病理学图像分割的生成式自监督

GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for  Histopathological Image Segmentation

Histopathological image segmentation is a challenging and important topic in
medical imaging with tremendous potential impact in clinical practice. State of
the art methods rely on hand-crafted annotations which hinder clinical
translation since histology suffers from significant variations between cancer
phenotypes. In this paper, we propose a weakly supervised framework for whole
slide imaging segmentation that relies on standard clinical annotations,
available in most medical systems. In particular, we exploit a multiple
instance learning scheme for training models. The proposed framework has been
evaluated on multi-locations and multi-centric public data from The Cancer
Genome Atlas and the PatchCamelyon dataset. Promising results when compared
with experts' annotations demonstrate the potentials of the presented approach.
The complete framework, including $6481$ generated tumor maps and data
processing, is available at this https URL

本文提出了一种弱监督整个切片图像分割的框架，利用标准的临床注释，采用多实例学习方案进行模型训练，并在 The Cancer Genome Atlas 和 PatchCamelyon 数据集上进行了评估，显示出一定的潜力.