Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China.
College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
Sci Rep. 2024 Sep 8;14(1):20892. doi: 10.1038/s41598-024-71650-6.
To solve the issue of diagnosis accuracy of diabetic retinopathy (DR) and reduce the workload of ophthalmologists, in this paper we propose a prior-guided attention fusion Transformer for multi-lesion segmentation of DR. An attention fusion module is proposed to improve the key generator to integrate self-attention and cross-attention and reduce the introduction of noise. The self-attention focuses on lesions themselves, capturing the correlation of lesions at a global scale, while the cross-attention, using pre-trained vessel masks as prior knowledge, utilizes the correlation between lesions and vessels to reduce the ambiguity of lesion detection caused by complex fundus structures. A shift block is introduced to expand association areas between lesions and vessels further and to enhance the sensitivity of the model to small-scale structures. To dynamically adjust the model's perception of features at different scales, we propose the scale-adaptive attention to adaptively learn fusion weights of feature maps at different scales in the decoder, capturing features and details more effectively. The experimental results on two public datasets (DDR and IDRiD) demonstrate that our model outperforms other state-of-the-art models for multi-lesion segmentation.
为了解决糖尿病视网膜病变（DR）诊断准确率的问题，降低眼科医生的工作量，本文提出了一种基于先验引导的注意力融合 Transformer 进行 DR 多病灶分割。提出了一种注意力融合模块，以改进关键生成器，从而整合自注意力和交叉注意力，减少引入的噪声。自注意力关注病灶本身，在全局尺度上捕捉病灶之间的相关性，而交叉注意力则利用预先训练好的血管蒙版作为先验知识，利用病灶和血管之间的相关性，减少由于复杂眼底结构引起的病灶检测的模糊性。引入移位块进一步扩展病灶和血管之间的关联区域，增强模型对小尺度结构的敏感性。为了动态调整模型在不同尺度上的特征感知，我们提出了尺度自适应注意力，自适应地学习解码器中不同尺度特征图的融合权重，从而更有效地捕捉特征和细节。在两个公共数据集（DDR 和 IDRiD）上的实验结果表明，我们的模型在多病灶分割方面优于其他最先进的模型。
