{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:22:12Z","timestamp":1771262532094,"version":"3.50.1"},"reference-count":0,"publisher":"TechForum Publishing Group","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bull. Comput. Data Sci."],"published-print":{"date-parts":[[2025,3,30]]},"abstract":"<jats:p>Whole-slide images (WSIs) in computational pathology contain morphological patterns across multiple spatial scales, yet most multiple instance learning (MIL) methods operate on a single resolution. Recent work shows that frequency-domain token mixing via the Fourier transform can improve both accuracy and efficiency over self-attention for WSI classification, but existing frequency-based MIL models still reason at only one scale. In this paper, we propose Multi-Scale FourierMIL (MS-FourierMIL), a hierarchical frequency-domain MIL framework that integrates patch tokens from multiple spatial resolutions. For each scale, we extract patch embeddings with a frozen feature extractor and apply a scale-specific Fourier token mixer based on a learnable all-pass filter, then perform cross-scale frequency fusion over pooled scale representations to capture interactions between coarse tissue architecture and fine cellular detail. A simple adaptive padding scheme stabilizes Fourier transforms for variable bag sizes while preserving token statistics, and a final class token conditioned on all scales produces slide-level predictions. On benchmark WSI classification tasks, MS-FourierMIL outperforms single-scale Fourier-based MIL, transformer-based MIL, and graph-based MIL baselines, particularly when lesion sizes and tissue context are highly variable, while maintaining competitive computational cost. Qualitative analyses of scale-specific attribution maps show that MS-FourierMIL focuses on global patterns at low resolution and localized tumor regions at high resolution, aligning with pathologists\u2019 multi-scale reasoning and highlighting frequency-domain multi-scale MIL as a promising strategy for accurate and efficient WSI analysis.<\/jats:p>","DOI":"10.71448\/bcds2561-1","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T16:33:31Z","timestamp":1771259611000},"page":"1-15","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Scale FourierMIL for Hierarchical Frequency-Domain Multiple Instance Learning in Whole-Slide Image Classification"],"prefix":"10.71448","volume":"6","author":[{"name":"The school of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"The school of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"The school of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"52394","published-online":{"date-parts":[[2025,3,30]]},"container-title":["Bulletin of Computer and Data Sciences"],"original-title":[],"deposited":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T16:33:31Z","timestamp":1771259611000},"score":1,"resource":{"primary":{"URL":"https:\/\/bcds.ch\/multi-scale-fouriermil-for-hierarchical-frequency-domain-multiple-instance-learning-in-whole-slide-image-classification\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,30]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,3,30]]},"published-print":{"date-parts":[[2025,3,30]]}},"URL":"https:\/\/doi.org\/10.71448\/bcds2561-1","relation":{},"ISSN":["3072-2926"],"issn-type":[{"value":"3072-2926","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,30]]}}}