【Author】
Wang, Xiaodong; He, Zhen'an; Wang, Ying; Dang, Linlin; Han, Weifang; Zhang, Cheng
【Source】WIRELESS COMMUNICATIONS & MOBILE COMPUTING
【Abstract】The intestine is an important organ of the human body, and its internal structure always needs to be observed in clinical applications so as to provide a basis for accurate diagnosis. However, due to the limited intestinal data obtained by a single institution, deep learning cannot effectively train the intestines, and the effect is not satisfied. For this reason, we propose a distributed training method to carry out federated learning to alleviate the situation of patient sample data shortage, not shared and uneven data distribution. And the blockchain is introduced to enhance the interaction between networks, to solve the problem of a single point of failure of the federated learning server. Fully excavate the multiscale features of samples, to construct a fusion enhancement model and intestinal segmentation module for accurate positioning. At the local end, the centerline extraction algorithm is optimized, with the edge as the main and the source as the auxiliary to realize centerline extraction.
【摘要】肠道是人体的重要器官，临床应用中始终需要观察其内部结构，为准确诊断提供依据。但由于单一机构获得的肠道数据有限，深度学习无法有效训练肠道，效果并不理想。为此，我们提出了一种分布式训练方法来进行联邦学习，以缓解患者样本数据短缺、不共享和数据分布不均匀的情况。并引入区块链增强网络之间的交互，解决联邦学习服务器的单点故障问题。充分挖掘样本的多尺度特征，构建融合增强模型和肠道分割模块，实现精准定位。在本地端优化中心线提取算法，以边缘为主，源为辅助，实现中心线提取。
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