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This equation captures one of the core mathematical components of the system. for pathology: For an input image X ∈RH×W×3, it is first parti-
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SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification explores A self-supervised hybrid state space model for pathological image classification that outperforms 11 state-of-the-art models.. Commercial viability score: 7/10 in Medical AI.
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Canonical ID ssmamba-a-self-supervised-hybrid-state-space-model-for-pathological-image-classification | Route /paper/ssmamba-a-self-supervised-hybrid-state-space-model-for-pathological-image-classification
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/buildability/ssmamba-a-self-supervised-hybrid-state-space-model-for-pathological-image-classification
Subject: SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification
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This equation captures one of the core mathematical components of the system. for pathology: For an input image X ∈RH×W×3, it is first parti-
Page and bbox are available; crop image is pending.
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Receipt path
/buildability/ssmamba-a-self-supervised-hybrid-state-space-model-for-pathological-image-classification
Paper ref
ssmamba-a-self-supervised-hybrid-state-space-model-for-pathological-image-classification
arXiv id
2604.15711
Generated at
2026-04-20T20:23:58.171Z
Evidence freshness
fresh
Last verification
2026-04-20T20:23:58.171Z
Sources
3
References
0
Coverage
50%
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05f11c2038aadfef7e2a84caeef4cfafb5e28191022423f793a4bdda4ec7cd02
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references
This equation captures one of the core mathematical components of the system. k/2 X Depthwise: Xdw[c, t] = i=−k/2 Wdw[c, i] · X[c, t + i]
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This equation captures one of the core mathematical components of the system. activation (Eq. 4) Require: Input sequence Xin ∈RL×d, sequence length L, hid- den dimension d Ensure: Output sequence
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