ScaleFusionNet：面向皮肤皮捣分割的 Transformer 指导下的多尺度特征融合

黑色素瘤是起源于皮肤细胞皮捣的恶性肿瘤。 accurate and efficient segmentation of skin lesions is essential for quantitative analysis but remains a challenge due to blurred lesion boundaries, gradual color changes, and irregular shapes. To address this, we propose ScaleFusionNet, a hybrid model that integrates a Cross-Attention Transformer Module (CATM) and adaptive fusion block (AFB) to enhance feature extraction and fusion by capturing both local and global features. We introduce CATM, which utilizes Swin transformer blocks and Cross Attention Fusion (CAF) to adaptively refine feature fusion and reduce semantic gaps in the encoder-decoder to improve segmentation accuracy. Additionally, the AFB uses Swin Transformer-based attention and deformable convolution-based adaptive feature extraction to help the model gather local and global contextual information through parallel pathways. This enhancement refines the lesion boundaries and preserves fine-grained details. ScaleFusionNet achieves Dice scores of 92.94% and 91.80% on the ISIC-2016 and ISIC-2018 datasets, respectively, demonstrating its effectiveness in skin lesion analysis. Simultaneously, independent validation experiments were conducted on the PH² dataset using the pretrained model weights. The results show that ScaleFusionNet demonstrates significant performance improvements compared with other state-of-the-art methods. Our code implementation is publicly available at GitHub.