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

• URL: http://arxiv.org/abs/2606.26157v1
• Date: Tue, 23 Jun 2026 20:57:22 GMT
• Title: Reducing Redundancy in Whole-Slide Image Patching for Scalable Indexing and Retrieval
• Abstract summary: We propose ARReST, 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.<n>By minimizing superfluous patch representations, ARReST reduces storage footprint, computational overhead, and accelerates similarity search across large pathology repositories.<n>The proposed strategy enables scalable, cost-efficient WSI indexing and is well-suited for next-generation retrieval-driven clinical AI systems.
• Score: 2.497936211748472
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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%$\pm$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.

Related papers

• Where to Refine, When to Stop: Rethinking Redundancy via Latent Discrepancy for Efficient Visual Autoregressive Generation [50.139984798361375]
  We propose a training-free framework that removes redundancy via latent discrepancy.<n> LD-Pruning substantially reduces inference while maintaining high generation quality, achieving up to 2.35x speedup on Infinity-8B.
  arXiv  Detail & Related papers  (2026-05-29T19:34:39Z)
• No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval [51.43543998583709]
  Single-stage Sparse Retrieval (SSR) is a paradigm shift that replaces expensive clustering with efficient sparse coding.<n>It reduces indexing time by 15x compared to ColBERTv2, halves retrieval latency, and simultaneously improves retrieval performance.
  arXiv  Detail & Related papers  (2026-05-28T15:53:34Z)
• ForensicZip: More Tokens are Better but Not Necessary in Forensic Vision-Language Models [108.5042835056188]
  We introduce ForensicZip, a training-free framework that reformulates token compression from a forgery-driven perspective.<n>ForensicZip achieves $2.97times$ speedup and over 90% FLOPs reduction while maintaining state-of-the-art detection performance.
  arXiv  Detail & Related papers  (2026-03-12T17:30:49Z)
• Learned Image Compression and Restoration for Digital Pathology [3.7263231077138608]
  Digital pathology images play a crucial role in medical diagnostics, but their ultra-high resolution and large file sizes pose significant challenges for storage, transmission, and real-time visualization.<n>We propose CLERIC, a novel deep learning-based image compression framework designed specifically for whole slide images (WSIs)<n> CLERIC integrates a learnable lifting scheme and advanced convolutional techniques to enhance compression efficiency while preserving critical pathological details.
  arXiv  Detail & Related papers  (2025-03-31T09:09:09Z)
• Pathology Image Compression with Pre-trained Autoencoders [52.208181380986524]
  Whole Slide Images in digital histopathology pose significant storage, transmission, and computational efficiency challenges.<n>Standard compression methods, such as JPEG, reduce file sizes but fail to preserve fine-grained phenotypic details critical for downstream tasks.<n>In this work, we repurpose autoencoders (AEs) designed for Latent Diffusion Models as an efficient learned compression framework for pathology images.
  arXiv  Detail & Related papers  (2025-03-14T17:01:17Z)
• A Short Survey on Set-Based Aggregation Techniques for Single-Vector WSI Representation in Digital Pathology [0.0]
  Digital pathology is revolutionizing the field of pathology by enabling the digitization, storage, and analysis of tissue samples as whole slide images (WSIs) WSIs are gigapixel files that capture the intricate details of tissue samples, providing a rich source of information for diagnostic and research purposes. Due to their enormous size, representing these images as one compact vector is essential for many computational pathology tasks.
  arXiv  Detail & Related papers  (2024-09-06T20:56:25Z)
• Semi-Parametric Retrieval via Binary Bag-of-Tokens Index [71.78109794895065]
  SemI-parametric Disentangled Retrieval (SiDR) is a bi-encoder retrieval framework that decouples retrieval index from neural parameters.<n>SiDR supports a non-parametric tokenization index for search, achieving BM25-like indexing complexity with significantly better effectiveness.
  arXiv  Detail & Related papers  (2024-05-03T08:34:13Z)
• SPLICE -- Streamlining Digital Pathology Image Processing [0.7852714805965528]
  We propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE) SPLICE condenses a histopathology WSI into a compact set of representative patches, forming a "collage" of WSI while minimizing redundancy. As an unsupervised method, SPLICE effectively reduces storage requirements for representing tissue images by 50%.
  arXiv  Detail & Related papers  (2024-04-26T21:30:36Z)
• Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer Compression [63.23578860867408]
  We investigate how to integrate the evaluations of importance and sparsity scores into a single stage. We present OFB, a cost-efficient approach that simultaneously evaluates both importance and sparsity scores. Experiments demonstrate that OFB can achieve superior compression performance over state-of-the-art searching-based and pruning-based methods.
  arXiv  Detail & Related papers  (2024-03-23T13:22:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.