IT-Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany.
Medical Data Integration Center, Institute of Digital Medicine, University Hospital Augsburg, Augsburg, Germany.
Stud Health Technol Inform. 2021 Sep 21;283:23-31. doi: 10.3233/SHTI210537.
Preventable or undiagnosed visual impairment and blindness affect billion of people worldwide. Automated multi-disease detection models offer great potential to address this problem via clinical decision support in diagnosis. In this work, we proposed an innovative multi-disease detection pipeline for retinal imaging which utilizes ensemble learning to combine the predictive capabilities of several heterogeneous deep convolutional neural network models. Our pipeline includes state-of-the-art strategies like transfer learning, class weighting, real-time image augmentation and Focal loss utilization. Furthermore, we integrated ensemble learning techniques like heterogeneous deep learning models, bagging via 5-fold cross-validation and stacked logistic regression models. Through internal and external evaluation, we were able to validate and demonstrate high accuracy and reliability of our pipeline, as well as the comparability with other state-of-the-art pipelines for retinal disease prediction.
可预防或未诊断的视力损害和失明影响着全球数十亿人。自动化多病种检测模型通过临床决策支持在诊断中具有很大的应用潜力。在这项工作中，我们提出了一种创新的视网膜成像多病种检测管道，该管道利用集成学习来结合几种异构深度卷积神经网络模型的预测能力。我们的管道包括转移学习、类别加权、实时图像增强和焦点损失利用等最新策略。此外，我们还集成了集成学习技术，如异构深度学习模型、通过 5 折交叉验证进行的袋装和堆叠逻辑回归模型。通过内部和外部评估，我们验证并证明了我们的管道具有高精度和高可靠性，并且与其他视网膜疾病预测的最新管道具有可比性。
