M²GRPO uses a Mamba-based policy and normalized group-relative advantages under CTDE to achieve higher pursuit success and capture efficiency than MAPPO and recurrent baselines in simulations and pool tests.
MARL-MambaContour: Unleashing multi-agent deep reinforcement learning for active contour optimization in medical image segmentation
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
SegTTA improves MedSAM2 zero-shot segmentation on uterus and liver datasets by test-time augmentations plus weighted voting, delivering +1.6 mIoU and -2.0 HD95 on multiclass hepatic vessels.
citing papers explorer
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M$^{2}$GRPO: Mamba-based Multi-Agent Group Relative Policy Optimization for Biomimetic Underwater Robots Pursuit
M²GRPO uses a Mamba-based policy and normalized group-relative advantages under CTDE to achieve higher pursuit success and capture efficiency than MAPPO and recurrent baselines in simulations and pool tests.
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SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation
SegTTA improves MedSAM2 zero-shot segmentation on uterus and liver datasets by test-time augmentations plus weighted voting, delivering +1.6 mIoU and -2.0 HD95 on multiclass hepatic vessels.