# Yottixel -- An Image Search Engine for Large Archives of Histopathology Whole Slide Images

arXiv: https://arxiv.org/abs/1911.08748 · HTML: https://mlsanity.com/paper/1911.08748/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 30 of 41)

- [Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology](https://mlsanity.com/paper/2405.11643/) (2024)
- [Decentralized Federated Learning through Proxy Model Sharing](https://mlsanity.com/paper/2111.11343/) (2021)
- [Towards a Visual-Language Foundation Model for Computational Pathology](https://mlsanity.com/paper/2307.12914/) (2023)
- [A General-Purpose Self-Supervised Model for Computational Pathology](https://mlsanity.com/paper/2308.15474/) (2023)
- [A Survey on Graph-Based Deep Learning for Computational Histopathology](https://mlsanity.com/paper/2107.00272/) (2021)
- [RudolfV: A Foundation Model by Pathologists for Pathologists](https://mlsanity.com/paper/2401.04079/) (2024)
- [Evidence-based diagnostic reasoning with multi-agent copilot for human pathology](https://mlsanity.com/paper/2506.20964/) (2025)
- [A Foundational Multimodal Vision Language AI Assistant for Human Pathology](https://mlsanity.com/paper/2312.07814/) (2023)
- [Rotation-Agnostic Image Representation Learning for Digital Pathology](https://mlsanity.com/paper/2311.08359/) (2023)
- [Learning to Predict RNA Sequence Expressions from Whole Slide Images with Applications for Search and Classification](https://mlsanity.com/paper/2203.13997/) (2022)
- [When is a Foundation Model a Foundation Model](https://mlsanity.com/paper/2309.11510/) (2023)
- [Analysis and Validation of Image Search Engines in Histopathology](https://mlsanity.com/paper/2401.03271/) (2024)
- [Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images](https://mlsanity.com/paper/2106.06623/) (2021)
- [Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images](https://mlsanity.com/paper/2303.00943/) (2023)
- [Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'](https://mlsanity.com/paper/2304.08297/) (2023)
- [Fusion of Multi-scale Heterogeneous Pathology Foundation Models for Whole Slide Image Analysis](https://mlsanity.com/paper/2510.27237/) (2025)
- [Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach](https://mlsanity.com/paper/2305.11728/) (2023)
- [Creating an Atlas of Normal Tissue for Pruning WSI Patching Through Anomaly Detection](https://mlsanity.com/paper/2310.03106/) (2023)
- [Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning](https://mlsanity.com/paper/2107.03602/) (2021)
- [Clinically Relevant Latent Space Embedding of Cancer Histopathology Slides through Variational Autoencoder Based Image Compression](https://mlsanity.com/paper/2303.13332/) (2023)
- [WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval](https://mlsanity.com/paper/2305.03383/) (2023)
- [Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Records](https://mlsanity.com/paper/2206.03003/) (2022)
- [Selection of Distinct Morphologies to Divide & Conquer Gigapixel Pathology Images](https://mlsanity.com/paper/2311.09902/) (2023)
- [Siamese Content-based Search Engine for a More Transparent Skin and Breast Cancer Diagnosis through Histological Imaging](https://mlsanity.com/paper/2401.08272/) (2024)
- [Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images](https://mlsanity.com/paper/2405.04211/) (2024)
- [Histopathology Multi-modal Embedding for Pathology Composed Retrieval](https://mlsanity.com/paper/2502.07221/) (2025)
- [A Similarity Measure of Histopathology Images by Deep Embeddings](https://mlsanity.com/paper/2107.13703/) (2021)
- [Multimodal Pathology Image Search Between H&E Slides and Multiplexed Immunofluorescent Images](https://mlsanity.com/paper/2306.06780/) (2023)
- [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)

## Abstract (arXiv)

With the emergence of digital pathology, searching for similar images in large archives has gained considerable attention. Image retrieval can provide pathologists with unprecedented access to the evidence embodied in already diagnosed and treated cases from the past. This paper proposes a search engine specialized for digital pathology, called Yottixel, a portmanteau for "one yotta pixel," alluding to the big-data nature of histopathology images. The most impressive characteristic of Yottixel is its ability to represent whole slide images (WSIs) in a compact manner. Yottixel can perform millions of searches in real-time with a high search accuracy and low storage profile. Yottixel uses an intelligent indexing algorithm capable of representing WSIs with a mosaic of patches by converting them into a small number of methodically extracted barcodes, called "Bunch of Barcodes" (BoB), the most prominent performance enabler of Yottixel. The performance of the prototype platform is qualitatively tested using 300 WSIs from the University of Pittsburgh Medical Center (UPMC) and 2,020 WSIs from The Cancer Genome Atlas Program (TCGA) provided by the National Cancer Institute. Both datasets amount to more than 4,000,000 patches of 1000x1000 pixels. We report three sets of experiments that show that Yottixel can accurately retrieve organs and malignancies, and its semantic ordering shows good agreement with the subjective evaluation of human observers.