A Multi-scale Linear-time Encoder for Whole-Slide Image Analysis
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908807911702528 |
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| author | Dwarampudi, Jagan Mohan Reddy Wong, Joshua Van Nguyen, Hien Banerjee, Tania |
| author_facet | Dwarampudi, Jagan Mohan Reddy Wong, Joshua Van Nguyen, Hien Banerjee, Tania |
| contents | We introduce Multi-scale Adaptive Recurrent Biomedical Linear-time Encoder (MARBLE), the first \textit{purely Mamba-based} multi-state multiple instance learning (MIL) framework for whole-slide image (WSI) analysis. MARBLE processes multiple magnification levels in parallel and integrates coarse-to-fine reasoning within a linear-time state-space model, efficiently capturing cross-scale dependencies with minimal parameter overhead. WSI analysis remains challenging due to gigapixel resolutions and hierarchical magnifications, while existing MIL methods typically operate at a single scale and transformer-based approaches suffer from quadratic attention costs. By coupling parallel multi-scale processing with linear-time sequence modeling, MARBLE provides a scalable and modular alternative to attention-based architectures. Experiments on five public datasets show improvements of up to \textbf{6.9\%} in AUC, \textbf{20.3\%} in accuracy, and \textbf{2.3\%} in C-index, establishing MARBLE as an efficient and generalizable framework for multi-scale WSI analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02918 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Multi-scale Linear-time Encoder for Whole-Slide Image Analysis Dwarampudi, Jagan Mohan Reddy Wong, Joshua Van Nguyen, Hien Banerjee, Tania Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Tissues and Organs We introduce Multi-scale Adaptive Recurrent Biomedical Linear-time Encoder (MARBLE), the first \textit{purely Mamba-based} multi-state multiple instance learning (MIL) framework for whole-slide image (WSI) analysis. MARBLE processes multiple magnification levels in parallel and integrates coarse-to-fine reasoning within a linear-time state-space model, efficiently capturing cross-scale dependencies with minimal parameter overhead. WSI analysis remains challenging due to gigapixel resolutions and hierarchical magnifications, while existing MIL methods typically operate at a single scale and transformer-based approaches suffer from quadratic attention costs. By coupling parallel multi-scale processing with linear-time sequence modeling, MARBLE provides a scalable and modular alternative to attention-based architectures. Experiments on five public datasets show improvements of up to \textbf{6.9\%} in AUC, \textbf{20.3\%} in accuracy, and \textbf{2.3\%} in C-index, establishing MARBLE as an efficient and generalizable framework for multi-scale WSI analysis. |
| title | A Multi-scale Linear-time Encoder for Whole-Slide Image Analysis |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Tissues and Organs |
| url | https://arxiv.org/abs/2602.02918 |