Virtual staining streamlines traditional staining procedures by digitally
generating stained images from unstained or differently stained images. While
conventional staining methods involve time-consuming chemical processes,
virtual staining offers an efficient and low infrastructure alternative.
Leveraging microscopy-based techniques, such as confocal microscopy,
researchers can expedite tissue analysis without the need for physical
sectioning. However, interpreting grayscale or pseudo-color microscopic images
remains a challenge for pathologists and surgeons accustomed to traditional
histologically stained images. To fill this gap, various studies explore
digitally simulating staining to mimic targeted histological stains. This paper
introduces a novel network, In-and-Out Net, specifically designed for virtual
staining tasks. Based on Generative Adversarial Networks (GAN), our model
efficiently transforms Reflectance Confocal Microscopy (RCM) images into
Hematoxylin and Eosin (H&E) stained images. We enhance nuclei contrast in RCM
images using aluminum chloride preprocessing for skin tissues. Training the
model with virtual H\&E labels featuring two fluorescence channels eliminates
the need for image registration and provides pixel-level ground truth. Our
contributions include proposing an optimal training strategy, conducting a
comparative analysis demonstrating state-of-the-art performance, validating the
model through an ablation study, and collecting perfectly matched input and
ground truth images without registration. In-and-Out Net showcases promising
results, offering a valuable tool for virtual staining tasks and advancing the
field of histological image analysis.

介绍了一种用于虚拟染色任务的新型网络 In-and-Out Net，基于生成对抗网络（GAN），能够将表皮镜下反射共聚焦显微镜（RCM）图像高效地转化为血红素和噻唑蓝（H&E）染色图像，提供了用于虚拟染色任务的有价值的工具，推动了组织学图像分析领域的发展。

内外网单色虚拟 HE 染色

Single color virtual H&E staining with In-and-Out Net

Reflectance Confocal Microscopy (RCM) is a non-invasive imaging technique
used in biomedical research and clinical dermatology. It provides virtual
high-resolution images of the skin and superficial tissues, reducing the need
for physical biopsies. RCM employs a laser light source to illuminate the
tissue, capturing the reflected light to generate detailed images of
microscopic structures at various depths. Recent studies explored AI and
machine learning, particularly CNNs, for analyzing RCM images. Our study
proposes a segmentation strategy based on textural features to identify
clinically significant regions, empowering dermatologists in effective image
interpretation and boosting diagnostic confidence. This approach promises to
advance dermatological diagnosis and treatment.

反射共聚焦显微镜（RCM）是一种非侵入性成像技术，用于生物医学研究和临床皮肤学。它提供了皮肤和表浅组织的虚拟高分辨率图像，减少了对物理活检的需求。RCM 采用激光光源照射组织，捕捉反射光生成不同深度微观结构的详细图像。最近的研究探索了 AI 和机器学习，特别是 CNN，用于分析 RCM 图像。本研究提出了一种基于纹理特征的分割策略，用于识别具有临床意义的区域，提升皮肤科医生的图像解释能力和诊断信心。这种方法有望推动皮肤病的诊断和治疗。

通过利用人工智能驱动的反射共聚焦显微镜分析提升诊断

Enhancing Diagnosis through AI-driven Analysis of Reflectance Confocal  Microscopy

Reflectance confocal microscopy (RCM) is an effective, non-invasive
pre-screening tool for skin cancer diagnosis, but it requires extensive
training and experience to assess accurately. There are few quantitative tools
available to standardize image acquisition and analysis, and the ones that are
available are not interpretable. In this study, we use a recurrent neural
network with attention on convolutional network features. We apply it to
delineate skin strata in vertically-oriented stacks of transverse RCM image
slices in an interpretable manner. We introduce a new attention mechanism
called Toeplitz attention, which constrains the attention map to have a
Toeplitz structure. Testing our model on an expert labeled dataset of 504 RCM
stacks, we achieve 88.17% image-wise classification accuracy, which is the
current state-of-art.

使用带有注意力机制的循环神经网络和 Toeplitz 注意力，对 RCM 图像进行分层并分类，可作为一种无创皮肤癌预筛查工具。测试结果显示，在 504 个 RCM 图像堆叠数据集上，分类准确率达到了 88.17％，达到了目前的最高水平。