The integrity of the reconstructed human epidermis generated in vitro could
be assessed using histological analyses combined with immunohistochemical
staining of keratinocyte differentiation markers. Computer-based analysis of
scanned tissue saves the expert time and may improve the accuracy of
quantification by eliminating interrater reliability issues. However, technical
differences during the preparation and capture of stained images and the
presence of multiple artifacts may influence the outcome of computational
methods. Using a dataset with 598 unannotated images showing cross-sections of
in vitro reconstructed human epidermis stained with DAB-based
immunohistochemistry reaction to visualize 4 different keratinocyte
differentiation marker proteins (filaggrin, keratin 10, Ki67, HSPA2) and
counterstained with hematoxylin, we developed an unsupervised method for the
detection and quantification of immunohistochemical staining. The proposed
pipeline includes the following steps: (i) color normalization to reduce the
variability of pixel intensity values in different samples; (ii) color
deconvolution to acquire color channels of the stains used; (iii) morphological
operations to find the background area of the image; (iv) automatic image
rotation; and (v) finding markers of human epidermal differentiation with
clustering. Also, we created a method to exclude images without DAB-stained
areas. The most effective combination of methods includes: (i) Reinhard's
normalization; (ii) Ruifrok and Johnston color deconvolution method; (iii)
proposed image rotation method based on boundary distribution of image
intensity; (iv) k-means clustering using DAB stain intensity. These results
should enhance the performance of quantitative analysis of protein markers in
reconstructed human epidermis samples and enable comparison of their spatial
distribution between different experimental conditions.

通过组织学分析和免疫组织化学染色的角化细胞分化标记物，可以评估体外重建的人类表皮的完整性。使用计算机分析扫描组织可节省专家时间，消除评价者之间的可靠性问题，从而提高定量方法的准确性。通过开发一种无监督方法，我们检测和定量了免疫组织化学染色，实现了自动旋转图像，找到人类表皮分化标记物并排除不具有染色区域的图像，这些方法将增强对重建人类表皮样本中蛋白标记的定量分析性能，并使得在不同实验条件下它们的空间分布得以比较。

EpidermaQuant：基于 H-DAB 染色的重建人表皮图像的无监督表皮分化标志物的检测与定量

EpidermaQuant: Unsupervised detection and quantification of epidermal  differentiation markers on H-DAB-stained images of reconstructed human  epidermis

This work addresses the detection of Helicobacter pylori a bacterium
classified since 1994 as class 1 carcinogen to humans. By its highest
specificity and sensitivity, the preferred diagnosis technique is the analysis
of histological images with immunohistochemical staining, a process in which
certain stained antibodies bind to antigens of the biological element of
interest. This analysis is a time demanding task, which is currently done by an
expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and
detect H. pylori as an anomaly in image staining. Unlike existing
classification approaches, an autoencoder is able to learn patterns in an
unsupervised manner (without the need of image annotations) with high
performance. In particular, our model has an overall 91% of accuracy with 86\%
sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori.

该研究使用自编码器学习健康组织的潜在模式，并通过图像染色中的 H. pylori 异常来检测，具有 91% 的准确率、86% 的敏感性、96% 的特异性和 0.97 的 AUC。