Immunohistochemistry (IHC) biomarkers are essential tools for reliable cancer
diagnosis and subtyping. It requires cross-staining comparison among Whole
Slide Images (WSIs) of IHCs and hematoxylin and eosin (H&E) slides. Currently,
pathologists examine the visually co-localized areas across IHC and H&E glass
slides for a final diagnosis, which is a tedious and challenging task.
Moreover, visually inspecting different IHC slides back and forth to analyze
local co-expressions is inherently subjective and prone to error, even when
carried out by experienced pathologists. Relying on digital pathology, we
propose Composite Biomarker Image (CBI) in this work. CBI is a single image
that can be composed using different filtered IHC biomarker images for better
visualization. We present a CBI image produced in two steps by the proposed
solution for better visualization and hence more efficient clinical workflow.
In the first step, IHC biomarker images are aligned with the H&E images using
one coordinate system and orientation. In the second step, the positive or
negative IHC regions from each biomarker image (based on the pathologists
recommendation) are filtered and combined into one image using a fuzzy
inference system. For evaluation, the resulting CBI images, from the proposed
system, were evaluated qualitatively by the expert pathologists. The CBI
concept helps the pathologists to identify the suspected target tissues more
easily, which could be further assessed by examining the actual WSIs at the
same suspected regions.

使用数字病理学，本文提出了一种名为 CBI 的复合生物标记物图像，它使用不同过滤的免疫组化生物标记物图像进行组合以实现更好的可视化，并且更有效的临床工作流程。

组合生物标志物图像在组织病理学中的高级可视化

Composite Biomarker Image for Advanced Visualization in Histopathology

In this paper, we aimed to provide a review and tutorial for researchers in
the field of medical imaging using language models to improve their tasks at
hand. We began by providing an overview of the history and concepts of language
models, with a special focus on large language models. We then reviewed the
current literature on how language models are being used to improve medical
imaging, emphasizing different applications such as image captioning, report
generation, report classification, finding extraction, visual question
answering, interpretable diagnosis, and more for various modalities and organs.
The ChatGPT was specially highlighted for researchers to explore more potential
applications. We covered the potential benefits of accurate and efficient
language models for medical imaging analysis, including improving clinical
workflow efficiency, reducing diagnostic errors, and assisting healthcare
professionals in providing timely and accurate diagnoses. Overall, our goal was
to bridge the gap between language models and medical imaging and inspire new
ideas and innovations in this exciting area of research. We hope that this
review paper will serve as a useful resource for researchers in this field and
encourage further exploration of the possibilities of language models in
medical imaging.

该研究综述了语言模型在医学成像领域中的应用，重点介绍了不同应用，如图像字幕、报告生成、报告分类、查找提取、视觉问答、可解释性诊断等，并探讨了语言模型在医学影像分析中的潜在优点，包括提高临床工作流程效率、降低诊断错误、协助医疗保健专业人员提供及时准确的诊断。