# On Image Search in Histopathology

arXiv: https://arxiv.org/abs/2401.08699 · HTML: https://mlsanity.com/paper/2401.08699/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 70 references that are in the map.
- First posted on arXiv 14 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution](https://mlsanity.com/paper/2307.16883/) (2023)
- [Similar Image Search for Histopathology: SMILY](https://mlsanity.com/paper/1901.11112/) (2019)
- [Medical Image Retrieval using Deep Convolutional Neural Network](https://mlsanity.com/paper/1703.08472/) (2017)
- [Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence](https://mlsanity.com/paper/1911.08736/) (2019)
- [Learning to Predict RNA Sequence Expressions from Whole Slide Images with Applications for Search and Classification](https://mlsanity.com/paper/2203.13997/) (2022)
- [Deep Barcodes for Fast Retrieval of Histopathology Scans](https://mlsanity.com/paper/1805.08833/) (2018)
- [On the Behavior of Convolutional Nets for Feature Extraction](https://mlsanity.com/paper/1703.01127/) (2017)
- [When is a Foundation Model a Foundation Model](https://mlsanity.com/paper/2309.11510/) (2023)
- [Hospital-Agnostic Image Representation Learning in Digital Pathology](https://mlsanity.com/paper/2204.02404/) (2022)
- [Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation](https://mlsanity.com/paper/1505.05212/) (2015)
- [LILE: Look In-Depth before Looking Elsewhere -- A Dual Attention Network using Transformers for Cross-Modal Information Retrieval in Histopathology Archives](https://mlsanity.com/paper/2203.01445/) (2022)
- [Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'](https://mlsanity.com/paper/2304.08297/) (2023)

## Built on by (most cited first, 12 of 12)

- [PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images](https://mlsanity.com/paper/2503.17970/) (2025)
- [SPLICE -- Streamlining Digital Pathology Image Processing](https://mlsanity.com/paper/2404.17704/) (2024)
- [On Validation of Search & Retrieval of Tissue Images in Digital Pathology](https://mlsanity.com/paper/2408.01570/) (2024)
- [A Short Survey on Set-Based Aggregation Techniques for Single-Vector WSI Representation in Digital Pathology](https://mlsanity.com/paper/2409.04615/) (2024)
- [Multimodal Learning for Scalable Representation of High-Dimensional Medical Data](https://mlsanity.com/paper/2409.13115/) (2024)
- [Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability](https://mlsanity.com/paper/2406.12897/) (2024)
- [Aggregation Schemes for Single-Vector WSI Representation Learning in Digital Pathology](https://mlsanity.com/paper/2501.17822/) (2025)
- [THIR: Topological Histopathological Image Retrieval](https://mlsanity.com/paper/2511.13170/) (2025)
- [mViSE: A Visual Search Engine for Analyzing Multiplex IHC Brain Tissue Images](https://mlsanity.com/paper/2512.11745/) (2025)
- [Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Data](https://mlsanity.com/paper/2605.00902/) (2026)
- [CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval](https://mlsanity.com/paper/2605.24253/) (2026)
- [Reducing Redundancy in Whole-Slide Image Patching for Scalable Indexing and Retrieval](https://mlsanity.com/paper/2606.26157/) (2026)

## Abstract (arXiv)

Pathology images of histopathology can be acquired from camera-mounted microscopes or whole slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent advancements in search technologies allow for implicit quantification of tissue morphology across diverse primary sites, facilitating comparisons and enabling inferences about diagnosis, and potentially prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this paper, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast and efficient image search methods in their work.