# Immunohistochemistry Biomarkers-Guided Image Search for Histopathology

arXiv: https://arxiv.org/abs/2304.12424 · HTML: https://mlsanity.com/paper/2304.12424/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 24 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slides](https://mlsanity.com/paper/2101.07903/) (2021)
- [Similar Image Search for Histopathology: SMILY](https://mlsanity.com/paper/1901.11112/) (2019)
- [Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence](https://mlsanity.com/paper/1911.08736/) (2019)
- [Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks](https://mlsanity.com/paper/1710.05726/) (2017)
- [Automatic Multi-Stain Registration of Whole Slide Images in Histopathology](https://mlsanity.com/paper/2107.14292/) (2021)

## Built on by (most cited first, 1 of 1)

- [Selection of Distinct Morphologies to Divide & Conquer Gigapixel Pathology Images](https://mlsanity.com/paper/2311.09902/) (2023)

## Abstract (arXiv)

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.