ScaleFusionNet: Transformer-Guided Multi-Scale Feature Fusion for Skin Lesion Segmentation
Journal:
arXiv
Published Date:
Mar 5, 2025
Abstract
Melanoma is a malignant tumor originating from skin cell lesions. Accurate
and efficient segmentation of skin lesions is essential for quantitative
medical analysis but remains challenging. To address this, we propose
ScaleFusionNet, a segmentation model that integrates Cross-Attention
Transformer Module (CATM) and AdaptiveFusionBlock to enhance feature extraction
and fusion. The model employs a hybrid architecture encoder that effectively
captures both local and global features. We introduce CATM, which utilizes Swin
Transformer Blocks and Cross Attention Fusion (CAF) to adaptively refine
encoder-decoder feature fusion, reducing semantic gaps and improving
segmentation accuracy. Additionally, the AdaptiveFusionBlock is improved by
integrating adaptive multi-scale fusion, where Swin Transformer-based attention
complements deformable convolution-based multi-scale feature extraction. This
enhancement refines lesion boundaries and preserves fine-grained details.
ScaleFusionNet achieves Dice scores of 92.94% and 91.65% on ISIC-2016 and
ISIC-2018 datasets, respectively, demonstrating its effectiveness in skin
lesion analysis. Our code implementation is publicly available at GitHub.
