Recently, various technologies have been introduced into digital pathology,
including artificial intelligence (AI) driven methods, in both areas of
pathological whole slide image (WSI) analysis and spatial transcriptomics (ST)
analysis. AI-driven WSI analysis utilizes the power of deep learning (DL),
expands the field of view for histopathological image analysis. On the other
hand, ST bridges the gap between tissue spatial analysis and biological
signals, offering the possibility to understand the spatial biology. However, a
major bottleneck in DL-based WSI analysis is the preparation of training
patterns, as hematoxylin \& eosin (H\&E) staining does not provide direct
biological evidence, such as gene expression, for determining the category of a
biological component. On the other hand, as of now, the resolution in ST is far
beyond that of WSI, resulting the challenge of further spatial analysis.
Although various WSI analysis tools, including QuPath, have cited the use of
WSI analysis tools in the context of ST analysis, its usage is primarily
focused on initial image analysis, with other tools being utilized for more
detailed transcriptomic analysis. As a result, the information hidden beneath
WSI has not yet been fully utilized to support ST analysis.
To bridge this gap, we introduce QuST, a QuPath extension designed to bridge
the gap between H\&E WSI and ST analyzing tasks. In this paper, we highlight
the importance of integrating DL-based WSI analysis and ST analysis in
understanding disease biology and the challenges in integrating these
modalities due to differences in data formats and analytical methods. The QuST
source code is hosted on GitHub and documentation is available at
this https URL

介绍了一种名为 QuST 的 QuPath 扩展，旨在解决数字病理学中深度学习和空间转录组学分析的数据格式和分析方法上的差异所导致的挑战。

QuST: QuPath 扩展的整合全层切片图像和空间转录组学分析

QuST: QuPath Extension for Integrative Whole Slide Image and Spatial  Transcriptomics Analysis

Whole slide image (WSI) analysis has become increasingly important in the
medical imaging community, enabling automated and objective diagnosis,
prognosis, and therapeutic-response prediction. However, in clinical practice,
the ever-evolving environment hamper the utility of WSI analysis models. In
this paper, we propose the FIRST continual learning framework for WSI analysis,
named ConSlide, to tackle the challenges of enormous image size, utilization of
hierarchical structure, and catastrophic forgetting by progressive model
updating on multiple sequential datasets. Our framework contains three key
components. The Hierarchical Interaction Transformer (HIT) is proposed to model
and utilize the hierarchical structural knowledge of WSI. The
Breakup-Reorganize (BuRo) rehearsal method is developed for WSI data replay
with efficient region storing buffer and WSI reorganizing operation. The
asynchronous updating mechanism is devised to encourage the network to learn
generic and specific knowledge respectively during the replay stage, based on a
nested cross-scale similarity learning (CSSL) module. We evaluated the proposed
ConSlide on four public WSI datasets from TCGA projects. It performs best over
other state-of-the-art methods with a fair WSI-based continual learning setting
and achieves a better trade-off of the overall performance and forgetting on
previous task

提出了首个连续学习框架 ConSlide，用于 WSI 分析，解决了巨大图像大小、层次结构利用和灾难性遗忘等挑战，包括三个关键组成部分：Hierarchical Interaction Transformer (HIT)、Breakup-Reorganize (BuRo) 回放方法和异步更新机制。在四个公共 WSI 数据集上评估了 ConSlide，并在 WSI 基础连续学习设置中表现最佳，实现了整体性能和对先前任务遗忘的更好权衡。

ConSlide: 异步分层互动变压器与分解 - 重新组织排练用于连续全组织切片图像分析

ConSlide: Asynchronous Hierarchical Interaction Transformer with  Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis

The deep neural network is a research hotspot for histopathological image
analysis, which can improve the efficiency and accuracy of diagnosis for
pathologists or be used for disease screening. The whole slide pathological
image can reach one gigapixel and contains abundant tissue feature information,
which needs to be divided into a lot of patches in the training and inference
stages. This will lead to a long convergence time and large memory consumption.
Furthermore, well-annotated data sets are also in short supply in the field of
digital pathology. Inspired by the pathologist's clinical diagnosis process, we
propose a weakly supervised deep reinforcement learning framework, which can
greatly reduce the time required for network inference. We use neural network
to construct the search model and decision model of reinforcement learning
agent respectively. The search model predicts the next action through the image
features of different magnifications in the current field of view, and the
decision model is used to return the predicted probability of the current field
of view image. In addition, an expert-guided model is constructed by
multi-instance learning, which not only provides rewards for search model, but
also guides decision model learning by the knowledge distillation method.
Experimental results show that our proposed method can achieve fast inference
and accurate prediction of whole slide images without any pixel-level
annotations.

提出了一种基于弱监督深度强化学习框架的深度神经网络方法，可以快速有效地进行数字病理学领域中含有丰富组织特征信息的高分辨率全幅病理图像分析，而不需要基于像素级别的像素注释数据集。