# Reducing Redundancy in Whole-Slide Image Patching for Scalable Indexing and Retrieval

arXiv: https://arxiv.org/abs/2606.26157 · HTML: https://mlsanity.com/paper/2606.26157/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 23 Jun 2026.

## Builds on (most cited first, 9 of 9)

- [Yottixel -- An Image Search Engine for Large Archives of Histopathology Whole Slide Images](https://mlsanity.com/paper/1911.08748/) (2019)
- [Bag of Visual Words and Fusion Methods for Action Recognition: Comprehensive Study and Good Practice](https://mlsanity.com/paper/1405.4506/) (2014)
- [On Image Search in Histopathology](https://mlsanity.com/paper/2401.08699/) (2024)
- [Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective](https://mlsanity.com/paper/2407.07841/) (2024)
- [MinMax Radon Barcodes for Medical Image Retrieval](https://mlsanity.com/paper/1610.00318/) (2016)
- [Analysis and Validation of Image Search Engines in Histopathology](https://mlsanity.com/paper/2401.03271/) (2024)
- [SPLICE -- Streamlining Digital Pathology Image Processing](https://mlsanity.com/paper/2404.17704/) (2024)
- [Selection of Distinct Morphologies to Divide & Conquer Gigapixel Pathology Images](https://mlsanity.com/paper/2311.09902/) (2023)
- [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)

The rapid growth of digital pathology has created an urgent need for efficient indexing and retrieval of whole slide images (WSIs). This need is intensified by emerging generative AI workflows, particularly retrieval-augmented generation (RAG), which require dependable similarity search to support high-stakes clinical decision-making. Yet the substantial cost of high-performance storage limits the scalability and accessibility of WSI indexing for many healthcare institutions. Consequently, methods that can reduce storage demands while preserving retrieval accuracy have become a critical research priority. We propose ARReST (Antithetical Redundancy Reduction Strategy), a principled oppositional framework that leverages redundancy across dissimilar tissue classes to markedly decrease the number of patches that must be indexed from each WSI. Instead of eliminating only within-class duplicates, ARReST identifies antithetical patches-those whose representations contribute minimally to cross-class discrimination-and prunes them from the searchable archive. This targeted reduction substantially compresses the index without sacrificing morphological diversity or retrieval fidelity. By minimizing superfluous patch representations, ARReST reduces storage footprint, lowers computational overhead, and accelerates similarity search across large pathology repositories. Extensive experiments on TCGA repository (The Cancer Genome Atlas with 21 organs) demonstrate that ARReST achieves significant index compression while maintaining competitive retrieval performance. The observed storage savings of 3% to 60% (14%±13%) can be reliably achieved without compromising retrieval performance for many organs. The proposed strategy enables scalable, cost-efficient WSI indexing and is well-suited for next-generation retrieval-driven clinical AI systems.