融合注意力机制的U型对称网络的医疗辅助算法落地与运用

Landing and Use of Medical Assistance Algorithms for U-Shaped Symmetric Networks Incorporating Attention Mechanisms

摘要: 本文提出了一个融合注意力机制的U型对称网络的医疗辅助算法，用于提高诊断的准确性和效率。利用深度学习技术，对医学影像和病例数据进行自动化分析。本文使用了DRIVE、LiTS和SIIM-ACR等数据集进行训练，在U-Net网络模型的基础上，创新网络编码器和解码器之间引入了注意力模块，以动态聚焦于图像中的关键特征。通过K折交叉验证来评估模型性能，并使用L2权重衰减正则化防止模型过拟合。在系统落地方面，设计了基于Django的医疗辅助小程序平台，用户可以通过微信小程序进入用户界面，实现医疗影像器官分割、在线问诊等功能。管理员则可以通过PC网页端对用户进行管理。该系统具有较高的准确性和效率，可以有效改善医疗资源分配不均的问题。

Abstract: In this paper, we propose a medical assistance algorithm for U-shaped symmetric networks incorporating an attention mechanism for improving the accuracy and efficiency of diagnosis. Deep learning techniques are used to automate the analysis of medical images and case data. In this paper, datasets such as DRIVE, LiTS and SIIM-ACR are used for training, and an attention module is introduced between the innovative network encoder and decoder based on the U-Net network model to dynamically focus on key features in the image. The model performance is evaluated by K-fold cross-validation and L2 weight decay regularization is used to prevent model overfitting. In terms of system implementation, a Django-based medical assistance applet platform is designed, which allows users to access the user interface through a WeChat applet to realize functions such as medical image organ segmentation and online consultation. The administrator can manage the users through the PC web terminal. The system has high accuracy and efficiency, and can effectively improve the problem of uneven distribution of medical resources.