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.
U- net: Convolutional networks for biomedical image segmen- tation
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UniSpector organizes visual prompt space with spatial-spectral and contrastive encoders to support open-set defect localization, beating baselines by at least 19.7% AP50b and 15.8% AP50m on the new Inspect Anything benchmark.
MHMamba combines a U-Net with multi-head Mamba, channel calibration, and adaptive skip fusion to improve 3D brain tumor segmentation accuracy and small-lesion sensitivity on BraTS datasets while retaining linear complexity.
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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UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting
UniSpector organizes visual prompt space with spatial-spectral and contrastive encoders to support open-set defect localization, beating baselines by at least 19.7% AP50b and 15.8% AP50m on the new Inspect Anything benchmark.
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MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation
MHMamba combines a U-Net with multi-head Mamba, channel calibration, and adaptive skip fusion to improve 3D brain tumor segmentation accuracy and small-lesion sensitivity on BraTS datasets while retaining linear complexity.