mlsanityData to 7 Oct 2026

Enabling Progressive Whole-slide Image Analysis with Multi-scale Pyramidal Network

arXiv 2602.01951 ↗First posted 2 Feb 2026cs.CVcited by 0

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Abstract (arXiv)

Multiple-instance Learning (MIL) is commonly used for computational pathology (CPath), where multi-scale features are essential for capturing both fine cellular details and broad tissue architecture. However, existing multi-scale MIL approaches typically rely on the inflexible multi-magnification inputs or the computationally expensive architectures. As pre-trained foundation models (FMs) become the trend for feature extraction and boost lightweight models, we rethink and explore a more efficient multi-scale MIL method. In this paper, we propose the Multi-scale Pyramidal Network (MSPN), a plug-and-play module for attention-based MIL. MSPN introduces progressive multi-scale whole-slide image analysis using only a single high-magnification input. It consists of (1) grid-based remapping that aggregates high-magnification features to derive spatially-aware coarse feature maps, and (2) the Coarse Guidance Network (CGN) that learns coarse contexts. We benchmark MSPN as an add-on module to 4 attention-based frameworks on 5 clinically relevant tasks with 2 foundation models, and a pre-trained MIL framework. Our results demonstrate that MSPN consistently improves MIL across the compared configurations and tasks, while being lightweight and easy-to-use.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 45 references that are in the map. First posted on arXiv 2 Feb 2026.

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22 of 45 references.

References · 22

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  12. 2021Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networkscited by 124
10 more
  1. 2020Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Imagescited by 81
  2. 2024A Clinical Benchmark of Public Self-Supervised Pathology Foundation Modelscited by 64
  3. 2022Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathologycited by 58
  4. 2022Differentiable Zooming for Multiple Instance Learning on Whole-Slide Imagescited by 43
  5. 2021UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia gradingcited by 33
  6. 2025Do Multiple Instance Learning Models Transfer?cited by 28
  7. 2023Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classificationcited by 27
  8. 2023Cross-scale Multi-instance Learning for Pathological Image Diagnosiscited by 11
  9. 2024Sm: enhanced localization in Multiple Instance Learning for medical imaging classificationcited by 10
  10. 2025SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markerscited by 8

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