自动皮肤分割是皮肤癌早期诊断的有效方法，可以最大限度地减少漏检率，及时治疗早期皮肤癌。然而，皮肤镜图像中纹理、大小、形状、病变位置的显着变化以及模糊的边界使得准确定位和分割病变变得极具挑战性。为了应对这些挑战，我们提出了一种名为 TG-Net 的新颖框架，它利用文本诊断信息来指导皮肤镜图像的分割。具体来说，TG-Net采用双流编码器-解码器架构。双流编码器包括用于提取图像特征的 Res2Net 和我们提出的用于提取文本特征的文本注意（TA）块。通过分层引导，将文本特征嵌入到图像特征提取过程中。此外，我们设计了一个多级融合（MLF）模块来合并更高级别的特征并生成全局特征图作为后续步骤的指导。在网络的解码阶段，三个多尺度反向注意模块（MSRA）利用局部特征和全局特征图来产生最终的分割结果。我们对三个可公开访问的数据集（即 ISIC 2017、HAM10000 和 PH2）进行了广泛的实验。实验结果表明 TG-Net 优于最先进的方法，验证了我们方法的可靠性。源代码可在 https://github.com/ukeLin/TG-Net 获取。版权所有 © 2024 Elsevier Ltd。保留所有权利。

Automatic skin segmentation is an efficient method for the early diagnosis of skin cancer, which can minimize the missed detection rate and treat early skin cancer in time. However, significant variations in texture, size, shape, the position of lesions, and obscure boundaries in dermoscopy images make it extremely challenging to accurately locate and segment lesions. To address these challenges, we propose a novel framework named TG-Net, which exploits textual diagnostic information to guide the segmentation of dermoscopic images. Specifically, TG-Net adopts a dual-stream encoder-decoder architecture. The dual-stream encoder comprises Res2Net for extracting image features and our proposed text attention (TA) block for extracting textual features. Through hierarchical guidance, textual features are embedded into the process of image feature extraction. Additionally, we devise a multi-level fusion (MLF) module to merge higher-level features and generate a global feature map as guidance for subsequent steps. In the decoding stage of the network, local features and the global feature map are utilized in three multi-scale reverse attention modules (MSRA) to produce the final segmentation results. We conduct extensive experiments on three publicly accessible datasets, namely ISIC 2017, HAM10000, and PH2. Experimental results demonstrate that TG-Net outperforms state-of-the-art methods, validating the reliability of our method. Source code is available at https://github.com/ukeLin/TG-Net.Copyright © 2024 Elsevier Ltd. All rights reserved.