动态实体掩码图扩散模型用于组织病理图像表示学习
Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation Learning
AI总结：
针对组织病理图像特征差异和标注缺乏问题，提出动态实体掩码图扩散模型H-MGDM，利用互补子图作为扩散条件和目标，增强表示学习，在多个数据集上验证了其性能。
摘要：
自然图像特征与组织病理图像固有特征之间的显著差异，使得将预训练模型从自然图像直接应用和迁移到组织病理学任务具有挑战性。此外，组织病理图像补丁中频繁缺乏标注，促使研究者探索自监督学习方法，如掩码重建，以从大量未标记数据中学习表示。至关重要的是，以往基于掩码的自监督学习方法往往忽视了实体之间的空间交互，而这些交互对于构建病理实体的准确表示至关重要。为应对这些挑战，构建实体图是一种有前景的方法。此外，扩散重建策略最近通过其随机强度噪声添加技术，在增强鲁棒学习表示方面表现出优越性能。因此，我们引入了H-MGDM，一种通过动态实体掩码图扩散模型的新型自监督组织病理图像表示学习方法。具体而言，我们提出在预训练期间分别使用互补子图作为潜在扩散条件和自监督目标。我们注意到，图可以嵌入实体的拓扑关系并增强表示。动态条件和目标可以改善病理精细重建。我们的模型在三个大型组织病理数据集上进行了预训练实验。H-MGDM的先进预测性能和可解释性在六个数据集上的分类和生存分析等综合下游任务中得到了清晰评估。我们的代码将在https://github.com/centurion-crawler/H-MGDM公开提供。
英文摘要：
Significant disparities between the features of natural images and those inherent to histopathological images make it challenging to directly apply and transfer pre-trained models from natural images to histopathology tasks. Moreover, the frequent lack of annotations in histopathology patch images has driven researchers to explore self-supervised learning methods like mask reconstruction for learning representations from large amounts of unlabeled data. Crucially, previous mask-based efforts in self-supervised learning have often overlooked the spatial interactions among entities, which are essential for constructing accurate representations of pathological entities. To address these challenges, constructing graphs of entities is a promising approach. In addition, the diffusion reconstruction strategy has recently shown superior performance through its random intensity noise addition technique to enhance the robust learned representation. Therefore, we introduce H-MGDM, a novel self-supervised Histopathology image representation learning method through the Dynamic Entity-Masked Graph Diffusion Model. Specifically, we propose to use complementary subgraphs as latent diffusion conditions and self-supervised targets respectively during pre-training. We note that the graph can embed entities' topological relationships and enhance representation. Dynamic conditions and targets can improve pathological fine reconstruction. Our model has conducted pretraining experiments on three large histopathological datasets. The advanced predictive performance and interpretability of H-MGDM are clearly evaluated on comprehensive downstream tasks such as classification and survival analysis on six datasets. Our code will be publicly available at https://github.com/centurion-crawler/H-MGDM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
