论文标题
蝙蝠形式：朝边界吸引轻量化变压器进行有效的医疗图像分割
BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation
论文作者
论文摘要
目的：诞生的变压器是最近引起了爆炸性的关注。但是，全球表示学习的艰巨的计算复杂性以及严格的窗口分配，阻碍了他们在医学图像细分中的部署。这项工作旨在解决变压器中的以上两个问题，以更好地分割医疗图像。方法：我们提出了一个边界吸引的轻型变压器（BATFORMER），该变压器可以构建跨尺度的全局相互作用，其计算复杂性较低，并在熵的指导下灵活地生成窗口。具体而言，为了充分探索变压器在远程依赖性建立中的好处，引入了跨尺度的全球变压器（CGT）模块，以共同利用多个小规模的特征图，以使其具有较低计算复杂性的更丰富的全球特征。鉴于形状建模在医学图像分割中的重要性，构建了一个边界感知的局部变压器（BLT）模块。在熵降低和保存计算复杂性降低和形状保存的指导下，BLT与会产生边界失真的刚性窗口分配不同，BLT采用自适应窗户分配方案。 Results: BATFormer achieves the best performance in Dice of 92.84%, 91.97%, 90.26%, and 96.30% for the average, right ventricle, myocardium, and left ventricle respectively on the ACDC dataset and the best performance in Dice, IoU, and ACC of 90.76%, 84.64%, and 96.76% respectively on the ISIC 2018 dataset.更重要的是，与最先进的方法相比，Batformer需要最少的模型参数和最低的计算复杂性。结论和意义：我们的结果表明，有必要开发自定义的变压器，以进行有效，更好的医学图像分割。
Objective: Transformers, born to remedy the inadequate receptive fields of CNNs, have drawn explosive attention recently. However, the daunting computational complexity of global representation learning, together with rigid window partitioning, hinders their deployment in medical image segmentation. This work aims to address the above two issues in transformers for better medical image segmentation. Methods: We propose a boundary-aware lightweight transformer (BATFormer) that can build cross-scale global interaction with lower computational complexity and generate windows flexibly under the guidance of entropy. Specifically, to fully explore the benefits of transformers in long-range dependency establishment, a cross-scale global transformer (CGT) module is introduced to jointly utilize multiple small-scale feature maps for richer global features with lower computational complexity. Given the importance of shape modeling in medical image segmentation, a boundary-aware local transformer (BLT) module is constructed. Different from rigid window partitioning in vanilla transformers which would produce boundary distortion, BLT adopts an adaptive window partitioning scheme under the guidance of entropy for both computational complexity reduction and shape preservation. Results: BATFormer achieves the best performance in Dice of 92.84%, 91.97%, 90.26%, and 96.30% for the average, right ventricle, myocardium, and left ventricle respectively on the ACDC dataset and the best performance in Dice, IoU, and ACC of 90.76%, 84.64%, and 96.76% respectively on the ISIC 2018 dataset. More importantly, BATFormer requires the least amount of model parameters and the lowest computational complexity compared to the state-of-the-art approaches. Conclusion and Significance: Our results demonstrate the necessity of developing customized transformers for efficient and better medical image segmentation.
