M4Fuse introduces a lightweight state-space MoE architecture with cross-scale dual-stage gating that reduces parameters by 62.63% and improves average performance by 0.09% on BraTS2019 and BraTS2021 even at half the usual input resolution.
Lightm-unet: Mamba assists in lightweight unet for medical image segmentation
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The CRL-2025 atlas is a spatiotemporal fetal brain MRI reference with detailed segmentations and 126-region parcellations from 159 fetuses, released with datasets and a multiclass segmentation tool.
MambaLiteUNet integrates Mamba into U-Net with adaptive fusion, local-global mixing, and cross-gated attention modules to reach 87.12% IoU and 93.09% Dice on skin lesion datasets while cutting parameters by 93.6%.
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.
DSVM-UNet improves VM-UNet by dual self-distillation, reaching state-of-the-art segmentation performance on ISIC2017, ISIC2018, and Synapse datasets.
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
citing papers explorer
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M\textsuperscript{4}Fuse: Lightweight State-Space MoE with a Cross-Scale Gating Bridge for Brain Tumor Segmentation
M4Fuse introduces a lightweight state-space MoE architecture with cross-scale dual-stage gating that reduces parameters by 62.63% and improves average performance by 0.09% on BraTS2019 and BraTS2021 even at half the usual input resolution.
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An MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis
The CRL-2025 atlas is a spatiotemporal fetal brain MRI reference with detailed segmentations and 126-region parcellations from 159 fetuses, released with datasets and a multiclass segmentation tool.
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MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation
MambaLiteUNet integrates Mamba into U-Net with adaptive fusion, local-global mixing, and cross-gated attention modules to reach 87.12% IoU and 93.09% Dice on skin lesion datasets while cutting parameters by 93.6%.
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APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.
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DSVM-UNet : Enhancing VM-UNet with Dual Self-distillation for Medical Image Segmentation
DSVM-UNet improves VM-UNet by dual self-distillation, reaching state-of-the-art segmentation performance on ISIC2017, ISIC2018, and Synapse datasets.
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A Survey of Mamba
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.