A Multi-scale Linear-time Encoder for Whole-Slide Image Analysis | Signal Canvas | ScienceToStartup

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A Multi-scale Linear-time Encoder for Whole-Slide Image Analysis

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Proof freshness: stale
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Display score: 8/10
Last proof check: 2026-03-17
Score updated: 2026-04-02
Score fresh until: 2026-05-02
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Coverage: 33%

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A Multi-scale Linear-time Encoder for Whole-Slide Image Analysis

Canonical ID a-multi-scale-linear-time-encoder-for-whole-slide-image-analysis | Route /signal-canvas/a-multi-scale-linear-time-encoder-for-whole-slide-image-analysis

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source_context

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Claim map

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Evidencepartial
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.
Implicationpartial
The abstract explicitly states this as a novel contribution.
Verificationpartial
partial
Evidencepartial
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.
Implicationpartial
This describes the core architectural innovation of MARBLE, as stated in the abstract.
Verificationpartial
partial
Evidencepartial
efficiently capturing cross-scale dependencies with minimal parameter overhead.
Implicationpartial
The abstract directly states this benefit of the MARBLE architecture.
Verificationpartial
partial
Evidencepartial
while existing MIL methods typically operate at a single scale and transformer-based approaches suffer from quadratic attention costs.
Implicationpartial
This highlights the limitations of prior art that MARBLE aims to overcome, as stated in the abstract.
Verificationpartial
partial
Evidencepartial
By coupling parallel multi-scale processing with linear-time sequence modeling, MARBLE provides a scalable and modular alternative to attention-based architectures.
Implicationpartial
This positions MARBLE as a superior alternative to existing methods, as stated in the abstract.
Verificationpartial
partial
Evidencepartial
Experiments on five public datasets show improvements of up to \textbf{6.9\%} in AUC
Implicationpartial
Specific quantitative results are provided in the abstract.
Verificationpartial
partial
Evidencepartial
\textbf{20.3\%} in accuracy
Implicationpartial
Specific quantitative results are provided in the abstract.
Verificationpartial
partial
Evidencepartial
and \textbf{2.3\%} in C-index
Implicationpartial
Specific quantitative results are provided in the abstract.
Verificationpartial
partial

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