Medical practitioners use a number of diagnostic tests to make a reliable
diagnosis. Traditionally, Haematoxylin and Eosin (H&E) stained glass slides
have been used for cancer diagnosis and tumor detection. However, recently a
variety of immunohistochemistry (IHC) stained slides can be requested by
pathologists to examine and confirm diagnoses for determining the subtype of a
tumor when this is difficult using H&E slides only. Deep learning (DL) has
received a lot of interest recently for image search engines to extract
features from tissue regions, which may or may not be the target region for
diagnosis. This approach generally fails to capture high-level patterns
corresponding to the malignant or abnormal content of histopathology images. In
this work, we are proposing a targeted image search approach, inspired by the
pathologists workflow, which may use information from multiple IHC biomarker
images when available. These IHC images could be aligned, filtered, and merged
together to generate a composite biomarker image (CBI) that could eventually be
used to generate an attention map to guide the search engine for localized
search. In our experiments, we observed that an IHC-guided image search engine
can retrieve relevant data more accurately than a conventional (i.e., H&E-only)
search engine without IHC guidance. Moreover, such engines are also able to
accurately conclude the subtypes through majority votes.

本研究提出了一种有针对性的图像搜索方法来改善肿瘤检测和癌症诊断。通过多种免疫组化染色技术，将图像合并生成复合标记物图像，并利用深度学习技术实现对癌变或异常组织信息的高效筛查，从而提高了搜索引擎的精确度。