Test-Time Instance Selection for Improved Whole Slide Image Analysis

[2608.14759] Test-Time Instance Selection for Improved Whole Slide Image Analysis Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Image and Video Processing arXiv:2608.14759 (eess) [Submitted on 14 Aug 2026] Title: Test-Time Instance Selection for Improved Whole Slide Image Analysis Authors: Quoc Anh Nguyen , Sunhong Park , Jin Tae Kwak View a PDF of the paper titled Test-Time Instance Selection for Improved Whole Slide Image Analysis, by Quoc Anh Nguyen and 2 other authors View PDF HTML (experimental) Abstract: Whole Slide Image (WSI) analysis has been widely studied for cancer diagnosis. Conventionally, a gigapixel WSI is divided into small patches and processed by Multiple Instance Learning (MIL) models. However, existing MIL models typically process all patches, many of which contain redundant or non-informative tissue patterns. Although recent approaches have focused on instance selection to identify discriminative patches and reduce redundancy, these selection modules still require additional training. In this work, we propose Test-Time Instance Selection (TTIS), a training-free, plug-and-play framework that selects compact yet representative patches during inference. TTIS further incorporates a multi-view ensemble strategy to integrate distinct facets of tissue morphology, enhancing robustness. Importantly, TTIS can be seamlessly integrated into existing MIL models without retraining or architectural changes, enabling flexible deployment. Extensive evaluations across multiple benchmarks demonstrate that our approach improves or matches baseline MIL performance across a range of classification and subtyping tasks. Our implementation code is available at this https URL Comments: Accepted at The 2nd MICCAI Workshop on Efficient Medical AI (EMA4MICCAI 2026) Subjects: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM) Cite as: arXiv:2608.14759 [eess.IV] (or arXiv:2608.14759v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2608.14759 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Quoc Anh Nguyen [ view email ] [v1] Fri, 14 Aug 2026 09:05:50 UTC (1,613 KB) Full-text links: Access Paper: View a PDF of the paper titled Test-Time Instance Selection for Improved Whole Slide Image Analysis, by Quoc Anh Nguyen and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.IV < prev | next > new | recent | 2026-08 Change to browse by: cs cs.CV eess q-bio q-bio.QM References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• AI or Real: Detecting Partially Altered Videos Under Resource-Constrained Environments#1034031
• SRPR-Net: Semantic and Relational Prompt Refinement for Automated SAM-based Instance Segmentation#1050409
• Learning to Reason with Persistent Object States for Video Instance Segmentation#1130120
• Towards Object Segmentation Mask Selection Using Specular Reflections#677503
• Towards Object Segmentation Mask Selection Using Specular Reflections#674305