Cai Lili, Hou Keke, Zhou Su
School of Biomedical Engineering, Guangzhou Xinhua University, Guangzhou, China.
School of Health Sciences, Guangzhou Xinhua University, Guangzhou, China.
Skin Res Technol. 2024 Aug;30(8):e13783. doi: 10.1111/srt.13783.
In recent years, the increasing prevalence of skin cancers, particularly malignant melanoma, has become a major concern for public health. The development of accurate automated segmentation techniques for skin lesions holds immense potential in alleviating the burden on medical professionals. It is of substantial clinical importance for the early identification and intervention of skin cancer. Nevertheless, the irregular shape, uneven color, and noise interference of the skin lesions have presented significant challenges to the precise segmentation. Therefore, it is crucial to develop a high-precision and intelligent skin lesion segmentation framework for clinical treatment.
A precision-driven segmentation model for skin cancer images is proposed based on the Transformer U-Net, called BiADATU-Net, which integrates the deformable attention Transformer and bidirectional attention blocks into the U-Net. The encoder part utilizes deformable attention Transformer with dual attention block, allowing adaptive learning of global and local features. The decoder part incorporates specifically tailored scSE attention modules within skip connection layers to capture image-specific context information for strong feature fusion. Additionally, deformable convolution is aggregated into two different attention blocks to learn irregular lesion features for high-precision prediction.
A series of experiments are conducted on four skin cancer image datasets (i.e., ISIC2016, ISIC2017, ISIC2018, and PH2). The findings show that our model exhibits satisfactory segmentation performance, all achieving an accuracy rate of over 96%.
Our experiment results validate the proposed BiADATU-Net achieves competitive performance supremacy compared to some state-of-the-art methods. It is potential and valuable in the field of skin lesion segmentation.
近年来，皮肤癌，尤其是恶性黑色素瘤的发病率不断上升，已成为公共卫生领域的一个主要关注点。开发用于皮肤病变的精确自动分割技术在减轻医学专业人员的负担方面具有巨大潜力。这对于皮肤癌的早期识别和干预具有重要的临床意义。然而，皮肤病变的形状不规则、颜色不均匀以及噪声干扰对精确分割提出了重大挑战。因此，开发一个用于临床治疗的高精度智能皮肤病变分割框架至关重要。
基于Transformer U-Net提出了一种用于皮肤癌图像的精度驱动分割模型，称为BiADATU-Net，它将可变形注意力Transformer和双向注意力块集成到U-Net中。编码器部分利用带有双注意力块的可变形注意力Transformer，允许自适应学习全局和局部特征。解码器部分在跳跃连接层中并入专门定制的scSE注意力模块，以捕获特定于图像的上下文信息以进行强大的特征融合。此外，可变形卷积被聚合到两个不同的注意力块中，以学习不规则病变特征以进行高精度预测。
在四个皮肤癌图像数据集（即ISIC2016、ISIC2017、ISIC2018和PH2）上进行了一系列实验。结果表明，我们的模型表现出令人满意的分割性能，所有准确率均超过96%。
我们的实验结果验证了所提出的BiADATU-Net与一些现有最先进方法相比具有具有竞争力的性能优势。它在皮肤病变分割领域具有潜力和价值。
