用少量标注数据，通过异常染色模式自动识别胃癌相关细菌。
Diagnosising Helicobacter pylori using AutoEncoders and Limited Annotations through Anomalous Staining Patterns in IHC Whole Slide Images
- 用自编码器学习正常组织特征，以重建误差衡量异常程度。
- 在245张切片上达91%准确率，仅需163个阳性标注。
- 适合病理初筛或辅助标注，减少专家工作量。
本研究针对免疫组化全切片图像中幽门螺杆菌（H. pylori）的检测问题。该任务依赖病理学家人工观察，耗时费力，且初始阶段标注数据有限。我们提出一种基于自编码器的方法，学习健康组织的潜在特征，并在HSV空间中量化图像重建误差。通过ROC分析确定最优阈值，结合样本中阳性区域占比判断是否感染。实验基于自建数据库，包含245张全切片图像，共1211个标注区域，其中仅163个为阳性。使用10折交叉验证，本方法在诊断中取得91%准确率、86%敏感度、96%特异度和0.97 AUC，优于基于预训练RedNet18与ViT特征的基准阈值法和SVM。结果表明，该浅层自编码器方法在极少标注下仍具竞争力，可作为快速定位感染区域的辅助工具。
原文摘要 · Abstract (English)
Purpose: This work addresses the detection of Helicobacter pylori (H. pylori) in histological images with immunohistochemical staining. This analysis is a time demanding task, currently done by an expert pathologist that visually inspects the samples. Given the effort required to localise the pathogen in images, a limited number of annotations might be available in an initial setting. Our goal is to design an approach that, using a limited set of annotations, is capable of obtaining results good enough to be used as a support tool. Methods: We propose to use autoencoders to learn the latent patterns of healthy patches and formulate a specific measure of the reconstruction error of the image in HSV space. ROC analysis is used to set the optimal threshold of this measure and the percentage of positive patches in a sample that determines the presence of H. pylori. Results: Our method has been tested on an own database of 245 Whole Slide Images (WSI) having 117 cases without H. pylori and different density of the bacteria in the remaining ones. The database has 1211 annotated patches, with only 163 positive patches. This dataset of positive annotations was used to train a baseline thresholding and an SVM using the features of a pre-trained RedNet18 and ViT models. A 10-fold cross-validation shows that our method has better performance with 91% accuracy, 86% sensitivity, 96% specificity and 0.97 AUC in the diagnosis of H. pylori. Conclusion: Unlike classification approaches, our shallow autoencoder with threshold adaptation for the detection of anomalous staining is able to achieve competitive results with a limited set of annotated data. This initial approach is good enough to be used as a guide for fast annotation of infected patches.
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