Graph convolutional networks (GCNs) have emerged as a powerful alternative to
multiple instance learning with convolutional neural networks in digital
pathology, offering superior handling of structural information across various
spatial ranges - a crucial aspect of learning from gigapixel H&E-stained whole
slide images (WSI). However, graph message-passing algorithms often suffer from
oversmoothing when aggregating a large neighborhood. Hence, effective modeling
of multi-range interactions relies on the careful construction of the graph.
Our proposed multi-scale GCN (MS-GCN) tackles this issue by leveraging
information across multiple magnification levels in WSIs. MS-GCN enables the
simultaneous modeling of long-range structural dependencies at lower
magnifications and high-resolution cellular details at higher magnifications,
akin to analysis pipelines usually conducted by pathologists. The
architecture's unique configuration allows for the concurrent modeling of
structural patterns at lower magnifications and detailed cellular features at
higher ones, while also quantifying the contribution of each magnification
level to the prediction. Through testing on different datasets, MS-GCN
demonstrates superior performance over existing single-magnification GCN
methods. The enhancement in performance and interpretability afforded by our
method holds promise for advancing computational pathology models, especially
in tasks requiring extensive spatial context.

图卷积网络（GCNs）在数字病理学中作为卷积神经网络的多实例学习强大替代品，能够优秀地处理各种空间范围内的结构信息，对于从吉格巴 H&E 染色全幻灯片图像（WSI）中学习至关重要。然而，图传递算法在聚合大邻域时往往存在过度平滑的问题，因此，多尺度 GCN（MS-GCN）通过利用 WSIs 中多个放大级别上的信息解决了这个问题。MS-GCN 能够同时对较低放大倍数下的长程结构依赖性和较高放大倍数下的高分辨率细胞细节进行建模，类似于病理学家通常进行的分析流程。该架构的独特配置允许同时对较低放大倍数下的结构模式和较高放大倍数下的细胞特征进行建模，并量化每个放大倍数级别对预测的贡献。通过在不同数据集上的测试，MS-GCN 在现有单放大倍数 GCN 方法上展现出优越的性能。我们的方法在性能和可解释性方面的提升有望推动计算病理学模型的发展，特别是在需要广泛空间背景的任务中。

将多尺度拓扑与金字塔图卷积神经网络集成于数字病理学中

Integrating multiscale topology in digital pathology with pyramidal  graph convolutional networks

Computational pathology models rarely utilise data that will not be available
for inference. This means most models cannot learn from highly informative data
such as additional immunohistochemical (IHC) stains and spatial
transcriptomics. We present TriDeNT, a novel self-supervised method for
utilising privileged data that is not available during inference to improve
performance. We demonstrate the efficacy of this method for a range of
different paired data including immunohistochemistry, spatial transcriptomics
and expert nuclei annotations. In all settings, TriDeNT outperforms other
state-of-the-art methods in downstream tasks, with observed improvements of up
to 101%. Furthermore, we provide qualitative and quantitative measurements of
the features learned by these models and how they differ from baselines.
TriDeNT offers a novel method to distil knowledge from scarce or costly data
during training, to create significantly better models for routine inputs.

TriDeNT 是一种新颖的自监督方法，能够利用训练过程中不可用的特权数据来改善性能，从而在计算病理学模型中达到超越其他先进方法的效果，特别适用于免疫组织化学、空间转录组学等配对数据的研究。

TriDeNT：组织病理学中特权知识蒸馏的三重深度网络训练

TriDeNT: Triple Deep Network Training for Privileged Knowledge  Distillation in Histopathology

There is a strong incentive to develop computational pathology models to i)
ease the burden of tissue typology annotation from whole slide histological
images; ii) transfer knowledge, e.g., tissue class separability from the
withheld source domain to the distributionally shifted unlabeled target domain,
and simultaneously iii) detect Open Set samples, i.e., unseen novel categories
not present in the training source domain. This paper proposes a highly
practical setting by addressing the abovementioned challenges in one fell
swoop, i.e., source-free Open Set domain adaptation (SF-OSDA), which addresses
the situation where a model pre-trained on the inaccessible source dataset can
be adapted on the unlabeled target dataset containing Open Set samples. The
central tenet of our proposed method is distilling knowledge from a
self-supervised vision transformer trained in the target domain. We propose a
novel style-based data augmentation used as hard positives for self-training a
vision transformer in the target domain, yielding strongly contextualized
embedding. Subsequently, semantically similar target images are clustered while
the source model provides their corresponding weak pseudo-labels with
unreliable confidence. Furthermore, we propose cluster relative maximum logit
score (CRMLS) to rectify the confidence of the weak pseudo-labels and compute
weighted class prototypes in the contextualized embedding space that are
utilized for adapting the source model on the target domain. Our method
significantly outperforms the previous methods, including open set detection,
test-time adaptation, and SF-OSDA methods, setting the new state-of-the-art on
three public histopathological datasets of colorectal cancer (CRC) assessment-
Kather-16, Kather-19, and CRCTP. Our code is available at
this https URL

这篇论文提出了一个高度实际倡导 our method outperforms specific open set detection, test-time adaptation, and SF-OSDA methods to establish a new state-of-the-art on three public histopathological datasets (Kather-16, Kather-19, and CRCTP) for colorectal cancer assessment.