MambaBack: Bridging Local Features and Global Contexts in Whole Slide Image Analysis explores MambaBack combines Mamba and Gated CNNs for Whole Slide Image analysis, preserving spatial locality and optimizing memory for efficient pathology diagnostics.. Commercial viability score: 8/10 in Medical AI.
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Canonical ID mambaback-bridging-local-features-and-global-contexts-in-whole-slide-image-analysis | Route /paper/mambaback-bridging-local-features-and-global-contexts-in-whole-slide-image-analysis
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/buildability/mambaback-bridging-local-features-and-global-contexts-in-whole-slide-image-analysis
Subject: MambaBack: Bridging Local Features and Global Contexts in Whole Slide Image Analysis
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This equation captures one of the core mathematical components of the system. methods for both training and inference stages. During training, we employ a batch size B = 8, maximum 4,096 tile featur
Page and bbox are available; crop image is pending.
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Receipt path
/buildability/mambaback-bridging-local-features-and-global-contexts-in-whole-slide-image-analysis
Paper ref
mambaback-bridging-local-features-and-global-contexts-in-whole-slide-image-analysis
arXiv id
2604.15729
Generated at
2026-04-20T20:23:11.566Z
Evidence freshness
fresh
Last verification
2026-04-20T20:23:11.566Z
Sources
3
References
0
Coverage
50%
Lineage hash
e64b37f97b4eb39fc91dd6f17169da8f42fd24514d7c2f825769f0b398820c9f
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This equation captures one of the core mathematical components of the system. size L (where L = 64). To maximize GPU saturation, we process all M chunks in parallel through the local 1D Gated CNN an
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. To tackle the sequence length challenge, the input tile sequence X ∈RN×D is partitioned into local segments. We define a
Page and bbox are available; crop image is pending.
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