发表机构
University of Cologne; University Hospital Heidelberg; University of Heidelberg; University Hospital Basel; Attikon University General Hospital; University Hospital Mainz; Frankfurt University Hospital（科隆大学; 海德堡大学医院; 海德堡大学; 巴塞尔大学医院; 阿提孔大学综合医院; 美因茨大学医院; 法兰克福大学医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于CNN-DNN混合架构的并行训练方法，通过图像分解与定位，在COVID-19 CT诊断中实现高效训练，显著提升性能并缩短训练时间，加速模型开发。
AI 中文摘要
人工智能在协助放射科医生进行基于影像的多种疾病诊断方面已显示出潜力。高效训练大型深度学习模型对于处理极大规模数据集或动态增长的疾病数据（如大流行情境下的数据）至关重要。在这项回顾性研究中，我们从德国三个不同中心收集了300例COVID-19和非COVID-19肺炎患者的CT扫描。我们研究了一种基于图像分解和定位的混合CNN-DNN网络模型，该模型天然支持深度学习模型的并行和高效训练。总共训练了156个具有三种不同架构的模型，以捕获不同层次的特征，最终形成12个患者级别的COVID-19诊断模型。我们测量了诊断性能以及时间节省。最高的准确率来自采用并行CNN-DNN方法的DenseNet121和3D CNN模型，其中DenseNet121在4×4×1子域下达到88.78%的训练准确率、76.67%的验证准确率和76.03%的测试准确率；3D CNN在4×4×1子域下分别达到87.72%的训练准确率、76.82%的验证准确率和74.86%的测试准确率。在3D CNN模型和4×4×2子域下，观察到并行训练时间最大减少31倍。我们的并行训练方法提高了效率和性能，使得快速模型开发成为可能，这对于大流行防范至关重要。
英文摘要
Artificial intelligence has shown promise in assisting radiologists in imaging-based diagnosis across a wide range of diseases. Efficient training of large deep learning models is essential to cope with extremely large data sets or dynamically growing disease data, like in a pandemic like situation. In this retrospective study, we collected 300 CT scans from COVID-19 and non-COVID-19 pneumonia patients from three different centers in Germany. We investigated a hybrid CNN-DNN network model based on image decomposition and localization that naturally supports parallel and efficient training of deep learning models. In total, 156 models with three different architectures were trained to capture features at different levels resulting in 12 patient-level COVID-19 diagnosis models. Diagnostic performance as well as time saving were measured. The highest accuracy was obtained from DenseNet121 and 3D CNN models with a parallel CNN-DNN approach, resulting in $88.78\%$ training, $76.67\%$ validation and $76.03\%$ test accuracy for the DenseNet121 with $4\times4\times1$ subdomains and $87.72\%$ training, $76.82\%$ validation and $74.86\%$ test accuracy, respectively, for the 3D CNN with $4\times4\times1$ subdomains. The strongest reduction in parallel training time by a factor of $31$ was observed for the 3D CNN model and $4\times4\times2$ subdomains. Our parallel training approach improves efficiency as well as performance enabling rapid model development, among others crucial for pandemic preparedness.
