用注意力与聚焦调制提升皮肤病变分割精度
TAFM-Net: A Novel Approach to Skin Lesion Segmentation Using Transformer Attention and Focal Modulation
- 结合自适应注意力与聚焦调制机制，增强特征表达
- 在ISIC2016/2017/2018上达93.64%/86.88%/92.88%的交并比
- 适合医学图像分析场景，提升分割可解释性
将现代计算机视觉技术融入临床流程有望提升皮肤病变分割效果。U-Net架构在此领域广泛应用，经多次迭代以应对不同临床环境下图像异质性带来的挑战，如光照差异、患者特征变化及毛发密度影响。为进一步提升分割性能，本文提出TAFM-Net，融合自适应变压器注意力（TA）与聚焦调制（FM）机制。模型采用EfficientNetV2B1作为编码器，利用TA增强空间与通道相关显著性；解码器通过密集连接结构，在跳跃连接中嵌入FM，强化特征强调，提升分割精度与可解释性。设计了一种新型动态损失函数，整合区域与边界信息，有效引导训练。在ISIC2016、ISIC2017和ISIC2018数据集上，该模型分别取得93.64%、86.88%和92.88%的交并比（Jaccard），展现出在真实场景中的应用潜力。
原文摘要 · Abstract (English)
Incorporating modern computer vision techniques into clinical protocols shows promise in improving skin lesion segmentation. The U-Net architecture has been a key model in this area, iteratively improved to address challenges arising from the heterogeneity of dermatologic images due to varying clinical settings, lighting, patient attributes, and hair density. To further improve skin lesion segmentation, we developed TAFM-Net, an innovative model leveraging self-adaptive transformer attention (TA) coupled with focal modulation (FM). Our model integrates an EfficientNetV2B1 encoder, which employs TA to enhance spatial and channel-related saliency, while a densely connected decoder integrates FM within skip connections, enhancing feature emphasis, segmentation performance, and interpretability crucial for medical image analysis. A novel dynamic loss function amalgamates region and boundary information, guiding effective model training. Our model achieves competitive performance, with Jaccard coefficients of 93.64\%, 86.88\% and 92.88\% in the ISIC2016, ISIC2017 and ISIC2018 datasets, respectively, demonstrating its potential in real-world scenarios.
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