Histopathological whole slide images (WSIs) classification has become a
foundation task in medical microscopic imaging processing. Prevailing
approaches involve learning WSIs as instance-bag representations, emphasizing
significant instances but struggling to capture the interactions between
instances. Additionally, conventional graph representation methods utilize
explicit spatial positions to construct topological structures but restrict the
flexible interaction capabilities between instances at arbitrary locations,
particularly when spatially distant. In response, we propose a novel dynamic
graph representation algorithm that conceptualizes WSIs as a form of the
knowledge graph structure. Specifically, we dynamically construct neighbors and
directed edge embeddings based on the head and tail relationships between
instances. Then, we devise a knowledge-aware attention mechanism that can
update the head node features by learning the joint attention score of each
neighbor and edge. Finally, we obtain a graph-level embedding through the
global pooling process of the updated head, serving as an implicit
representation for the WSI classification. Our end-to-end graph representation
learning approach has outperformed the state-of-the-art WSI analysis methods on
three TCGA benchmark datasets and in-house test sets. Our code is available at
this https URL

我们提出了一种新颖的动态图表示算法，将组织病理学切片图像（WSIs）概念化为知识图结构的形式，通过动态构建邻居和带向边嵌入的头尾实例之间的关系，利用知识感知的注意机制更新头结点特征，然后通过全局池化过程获得图级嵌入，作为 WSI 分类的隐式表示。我们的端到端图表示学习方法在三个 TCGA 基准数据集和内部测试集上优于最先进的 WSI 分析方法。

知识感知注意力的动态图表示在组织病理全幻灯片图像分析中的应用

Dynamic Graph Representation with Knowledge-aware Attention for  Histopathology Whole Slide Image Analysis

Graph representation learning (also known as network embedding) has been
extensively researched with varying levels of granularity, ranging from nodes
to graphs. While most prior work in this area focuses on node-level
representation, limited research has been conducted on graph-level embedding,
particularly for dynamic or temporal networks. However, learning
low-dimensional graph-level representations for dynamic networks is critical
for various downstream graph retrieval tasks such as temporal graph similarity
ranking, temporal graph isomorphism, and anomaly detection. In this paper, we
present a novel method for temporal graph-level embedding that addresses this
gap. Our approach involves constructing a multilayer graph and using a modified
random walk with temporal backtracking to generate temporal contexts for the
graph's nodes. We then train a "document-level" language model on these
contexts to generate graph-level embeddings. We evaluate our proposed model on
five publicly available datasets for the task of temporal graph similarity
ranking, and our model outperforms baseline methods. Our experimental results
demonstrate the effectiveness of our method in generating graph-level
embeddings for dynamic networks.

本研究提出了一种为动态网络生成图层嵌入的新方法，包括构建多层图和使用改进的随机游走生成节点的时间上下文，再利用一个深度学习模型生成图层嵌入，并在公开数据集上进行了评估和比较。