Optimizing Neural Network Architecture for Medical Image Segmentation Using Monte Carlo Tree Search
This work addresses the need for more efficient and lightweight segmentation models in medical imaging, though it is incremental as it builds on existing NAS methods.
This paper tackled the problem of inefficient neural architecture search for medical image segmentation by proposing MNAS-Unet, which uses Monte Carlo Tree Search to dynamically explore architectures, resulting in a 54% reduction in search budget and competitive accuracy on datasets like PROMISE12.
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.