Medical image segmentation is pivotal in healthcare, enhancing diagnostic accuracy, informing treatment strategies, and tracking disease progression. This process allows clinicians to extract critical information from visual data, enabling personalized patient care. However, developing neural networks for segmentation remains challenging, especially when preserving image resolution, which is essential in detecting subtle details that influence diagnoses. Moreover, the lack of transparency in these deep learning models has slowed their adoption in clinical practice. Efforts in model interpretability are increasingly focused on making these models' decision-making processes more transparent. In this paper, we introduce MAPUNetR, a novel architecture that synergizes the strengths of transformer models with the proven U-Net framework for medical image segmentation. Our model addresses the resolution preservation challenge and incorporates attention maps highlighting segmented regions, increasing accuracy and interpretability. Evaluated on the BraTS 2020 dataset, MAPUNetR achieved a dice score of 0.88 and a dice coefficient of 0.92 on the ISIC 2018 dataset. Our experiments show that the model maintains stable performance and potential as a powerful tool for medical image segmentation in clinical practice.

本研究针对医学图像分割中的分辨率保持和模型透明性两个关键问题，提出了一种新颖的架构MAPUNetR，它结合了变换器模型与U-Net框架的优势。实验结果显示，MAPUNetR在BraTS 2020数据集上获得了0.88的Dice分数，0.92的Dice系数，表明该模型在临床实践中具有明显的性能优势和潜在影响。

MAPUNetR：高效且可解释的医学图像分割混合视觉变换器与 U-Net 架构