利用蒙特卡洛树搜索优化医疗图像分割的神经网络架构
Optimizing Neural Network Architecture for Medical Image Segmentation Using Monte Carlo Tree Search
AI总结：
本文提出MNAS-Unet框架，结合MCTS和NAS优化医疗图像分割架构，提升搜索效率并实现轻量模型。
摘要：
本文提出了一种新颖的医疗图像分割框架MNAS-Unet，结合了蒙特卡洛树搜索(MCTS)和神经架构搜索(NAS)。MNAS-Unet通过MCTS动态探索有前途的网络架构，显著提高了架构搜索的效率和准确性。它还优化了DownSC和UpSC单元结构，实现了快速且精确的模型调整。实验结果表明，MNAS-Unet在多个医疗图像数据集上，包括PROMISE12、超声神经和CHAOS，均优于NAS-Unet和其他最先进的模型，在分割准确性方面表现突出。此外，与NAS-Unet相比，MNAS-Unet将架构搜索预算减少了54%（在相同搜索设置下，提前停止于139个周期而非300个周期），同时实现了仅0.6M参数的轻量模型，并且GPU内存消耗更低，进一步提高了其实际应用性。这些结果表明，MNAS-Unet可以在实际资源限制下提高搜索效率，同时保持具有竞争力的分割准确性。
英文摘要：
This paper proposes a novel medical image segmentation framework, MNAS-Unet, which combines Monte Carlo Tree Search (MCTS) and Neural Architecture Search (NAS). MNAS-Unet dynamically explores promising network architectures through MCTS, significantly enhancing the efficiency and accuracy of architecture search. It also optimizes the DownSC and UpSC unit structures, enabling fast and precise model adjustments. Experimental results demonstrate that MNAS-Unet outperforms NAS-Unet and other state-of-the-art models in segmentation accuracy on several medical image datasets, including PROMISE12, Ultrasound Nerve, and CHAOS. Furthermore, compared with NAS-Unet, MNAS-Unet reduces the architecture search budget by 54% (early stopping at 139 epochs versus 300 epochs under the same search setting), while achieving a lightweight model with only 0.6M parameters and lower GPU memory consumption, which further improves its practical applicability. These results suggest that MNAS-Unet can improve search efficiency while maintaining competitive segmentation accuracy under practical resource constraints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
