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  3. Decoding Matters: Efficient Mamba-Based Decoder with Distrib
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Decoding Matters: Efficient Mamba-Based Decoder with Distribution-Aware Deep Supervision for Medical Image Segmentation

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Evidence fresh

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: Decoding Matters: Efficient Mamba-Based Decoder with Distribution-Aware Deep Supervision for Medical Image Segmentation

PDF: https://arxiv.org/pdf/2603.12547v1

Source count: 0

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

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Decoding Matters: Efficient Mamba-Based Decoder with Distribution-Aware Deep Supervision for Medical Image Segmentation

Overall score: 7/10
Lineage: 4f3f35af1d20…
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Canonical Paper Receipt

Last verification: 2026-04-02T02:30:40.136Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 17%

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Dimensions overall score 7.0

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Keep exploring

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Multi-View Deformable Convolution Meets Visual Mamba for Coronary Artery Segmentation
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Concept-to-Pixel: Prompt-Free Universal Medical Image Segmentation
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Progressive Split Mamba: Effective State Space Modelling for Image Restoration
Score 3.0down
Builds On This
Improving Generalization of Deep Learning for Brain Metastases Segmentation Across Institutions
Score 6.0down
Prior Work
SegMaFormer: A Hybrid State-Space and Transformer Model for Efficient Segmentation
Score 7.0stable
Prior Work
CLEAR-Mamba:Towards Accurate, Adaptive and Trustworthy Multi-Sequence Ophthalmic Angiography Classification
Score 7.0stable
Prior Work
Multi-Level Bidirectional Decoder Interaction for Uncertainty-Aware Breast Ultrasound Analysis
Score 7.0stable
Competing Approach
DCAU-Net: Differential Cross Attention and Channel-Spatial Feature Fusion for Medical Image Segmentation
Score 7.0stable

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  • How can AI improve the accuracy of medical image segmentation?(question)

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