Due to its superior efficiency in utilizing annotations and addressing
gigapixel-sized images, multiple instance learning (MIL) has shown great
promise as a framework for whole slide image (WSI) classification in digital
pathology diagnosis. However, existing methods tend to focus on advanced
aggregators with different structures, often overlooking the intrinsic features
of H\&E pathological slides. To address this limitation, we introduced two
pathological priors: nuclear heterogeneity of diseased cells and spatial
correlation of pathological tiles. Leveraging the former, we proposed a data
augmentation method that utilizes stain separation during extractor training
via a contrastive learning strategy to obtain instance-level representations.
We then described the spatial relationships between the tiles using an
adjacency matrix. By integrating these two views, we designed a multi-instance
framework for analyzing H\&E-stained tissue images based on pathological
inductive bias, encompassing feature extraction, filtering, and aggregation.
Extensive experiments on the Camelyon16 breast dataset and TCGA-NSCLC Lung
dataset demonstrate that our proposed framework can effectively handle tasks
related to cancer detection and differentiation of subtypes, outperforming
state-of-the-art medical image classification methods based on MIL. The code
will be released later.

该研究提出了一种基于病理先验的多实例学习框架，通过数据增强方法和空间相关性分析，能够有效地用于癌症检测和亚型区分的医学图像分类任务。

基于弱监督学习的病理切片整体分类

The Whole Pathological Slide Classification via Weakly Supervised  Learning

Cancer diagnosis, prognosis, and therapeutic response prediction are heavily
influenced by the relationship between the histopathological structures and the
function of the tissue. Recent approaches acknowledging the structure-function
relationship, have linked the structural and spatial patterns of cell
organization in tissue via cell-graphs to tumor grades. Though cell
organization is imperative, it is insufficient to entirely represent the
histopathological structure. We propose a novel hierarchical
cell-to-tissue-graph (HACT) representation to improve the structural depiction
of the tissue. It consists of a low-level cell-graph, capturing cell morphology
and interactions, a high-level tissue-graph, capturing morphology and spatial
distribution of tissue parts, and cells-to-tissue hierarchies, encoding the
relative spatial distribution of the cells with respect to the tissue
distribution. Further, a hierarchical graph neural network (HACT-Net) is
proposed to efficiently map the HACT representations to histopathological
breast cancer subtypes. We assess the methodology on a large set of annotated
tissue regions of interest from H\&E stained breast carcinoma whole-slides.
Upon evaluation, the proposed method outperformed recent convolutional neural
network and graph neural network approaches for breast cancer multi-class
subtyping. The proposed entity-based topological analysis is more inline with
the pathological diagnostic procedure of the tissue. It provides more command
over the tissue modelling, therefore encourages the further inclusion of
pathological priors into task-specific tissue representation.

研究提出了一种新颖的分层细胞 - 组织图（HACT）表示，以及一种分层图神经网络（HACT-Net），用于将 HACT 表示映射到组织学乳腺癌亚型中。该方法通过实体为基础的拓扑分析更符合组织病理诊断程序，从而鼓励进一步将病理学先验纳入任务特定的组织表示。