Abstract
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.
Abstract (translated)
本文提出了一种新颖的医学图像分割框架MNAS-Unet，该框架结合了蒙特卡洛树搜索（MCTS）和神经架构搜索（NAS）。MNAS-Unet通过MCTS动态探索有前景的网络结构，从而显著提高架构搜索的效率和准确性。此外，它还优化了DownSC和UpSC单元结构，使其能够快速准确地调整模型。 实验结果表明，在多个医学图像数据集上（包括PROMISE12、超声神经和CHAOS），MNAS-Unet在分割精度方面超越了NAS-Unet及其他最先进的模型。与NAS-Unet相比，在相同的搜索设置下，MNAS-Unet将架构搜索预算减少了54%（提前停止于139个时期而其他情况是300个时期），同时获得了仅包含0.6M参数的轻量级模型，并且降低了GPU内存消耗，从而进一步提升了其实用性。 这些结果表明，在实际资源约束条件下，MNAS-Unet能够在提高搜索效率的同时保持竞争力的分割精度。
URL
https://arxiv.org/abs/2602.22361
