IEEE Trans Med Imaging. 2018 Jul;37(7):1641-1652. doi: 10.1109/TMI.2018.2796130.
Histopathological image classification (HIC) and content-based histopathological image retrieval (CBHIR) are two promising applications for the histopathological whole slide image (WSI) analysis. HIC can efficiently predict the type of lesion involved in a histopathological image. In general, HIC can aid pathologists in locating high-risk cancer regions from a WSI by providing a cancerous probability map for the WSI. In contrast, CBHIR was developed to allow searches for regions with similar content for a region of interest (ROI) from a database consisting of historical cases. Sets of cases with similar content are accessible to pathologists, which can provide more valuable references for diagnosis. A drawback of the recent CBHIR framework is that a query ROI needs to be manually selected from a WSI. An automatic CBHIR approach for a WSI-wise analysis needs to be developed. In this paper, we propose a novel aided-diagnosis framework of breast cancer using whole slide images, which shares the advantages of both HIC and CBHIR. In our framework, CBHIR is automatically processed throughout the WSI, based on which a probability map regarding the malignancy of breast tumors is calculated. Through the probability map, the malignant regions in WSIs can be easily recognized. Furthermore, the retrieval results corresponding to each sub-region of the WSIs are recorded during the automatic analysis and are available to pathologists during their diagnosis. Our method was validated on fully annotated WSI data sets of breast tumors. The experimental results certify the effectiveness of the proposed method.
组织病理学图像分类（HIC）和基于内容的组织病理学图像检索（CBHIR）是组织病理学全切片图像（WSI）分析的两个有前途的应用。HIC 可以有效地预测组织病理学图像中涉及的病变类型。通常，HIC 可以通过为 WSI 提供癌性概率图来帮助病理学家从 WSI 中定位高风险的癌症区域。相比之下，CBHIR 的开发是为了允许从由历史病例组成的数据库中针对感兴趣区域（ROI）搜索具有相似内容的区域。具有相似内容的病例集可供病理学家访问，这可为诊断提供更有价值的参考。最近的 CBHIR 框架的一个缺点是需要从 WSI 中手动选择查询 ROI。需要开发一种用于 WSI 分析的自动 CBHIR 方法。在本文中，我们提出了一种使用全切片图像进行乳腺癌辅助诊断的新框架，该框架结合了 HIC 和 CBHIR 的优点。在我们的框架中，基于 CBHIR 会自动处理整个 WSI，并在此基础上计算出关于乳腺癌肿瘤恶性程度的概率图。通过概率图，可以轻松识别 WSI 中的恶性区域。此外，在自动分析过程中会记录与 WSI 的每个子区域相对应的检索结果，并在病理学家诊断时提供给他们。我们的方法在经过充分注释的乳腺癌 WSI 数据集上进行了验证。实验结果证明了所提出方法的有效性。
