Melanoma, a dangerous type of skin cancer resulting from abnormal skin cell growth, can be treated if detected early. Various approaches using Fully Convolutional Networks (FCNs) have been proposed, with the U-Net architecture being prominent To aid in its diagnosis through automatic skin lesion segmentation. However, the symmetrical U-Net model's reliance on convolutional operations hinders its ability to capture long-range dependencies crucial for accurate medical image segmentation. Several Transformer-based U-Net topologies have recently been created to overcome this limitation by replacing CNN blocks with different Transformer modules to capture local and global representations. Furthermore, the U-shaped structure is hampered by semantic gaps between the encoder and decoder. This study intends to increase the network's feature re-usability by carefully building the skip connection path. Integrating an already calculated attention affinity within the skip connection path improves the typical concatenation process utilized in the conventional skip connection path. As a result, we propose a U-shaped hierarchical Transformer-based structure for skin lesion segmentation and an Inter-scale Context Fusion (ISCF) method that uses attention correlations in each stage of the encoder to adaptively combine the contexts from each stage to mitigate semantic gaps. The findings from two skin lesion segmentation benchmarks support the ISCF module's applicability and effectiveness. The code is publicly available at \url{https://github.com/saniaesk/skin-lesion-segmentation}

翻译：黑色素瘤是一种由皮肤细胞异常生长引起的危险皮肤癌，若早期发现则可治疗。为辅助其诊断，已提出多种基于全卷积网络（FCN）的自动皮肤病灶分割方法，其中U-Net架构尤为突出。然而，对称的U-Net模型依赖卷积运算，这阻碍了其捕捉医学图像精确分割所需的长距离依赖关系。近期，研究人员通过用不同Transformer模块替换CNN模块以捕捉局部和全局表征，开发了多种基于Transformer的U-Net拓扑结构来克服这一局限。此外，U形结构还受到编码器与解码器之间语义差距的制约。本研究旨在通过精心构建跳跃连接路径来增强网络特征的复用性。在跳跃连接路径中集成已计算的注意力关联，改进了传统跳跃连接路径中使用的常规拼接过程。由此，我们提出一种用于皮肤病灶分割的U形层次化Transformer结构，以及一种跨尺度上下文融合（ISCF）方法——该方法利用编码器各阶段的注意力相关性自适应融合各阶段上下文，以缓解语义差距。两个皮肤病灶分割基准测试的结果验证了ISCF模块的适用性和有效性。代码已开源在\url{https://github.com/saniaesk/skin-lesion-segmentation}。