论文标题
深度卷积神经网络的概括 - 开源胸部X光片的案例研究
Generalization of Deep Convolutional Neural Networks -- A Case-study on Open-source Chest Radiographs
论文作者
论文摘要
深度卷积神经网络（DCNN）引起了广泛的关注，并在许多领域进行了应用，包括医疗图像分析和临床诊断。一个主要的挑战是在内部和外部数据上构想具有出色性能的DCNN模型。我们证明DCNN可能不会推广到新数据，但是提高培训数据的质量和异质性有助于提高普遍性因素。我们使用InceptionResnetv2和Densenet121架构来预测5种常见胸部病理的风险。实验是在三个公开可用的数据库上进行的：CHEXPERT，CHESTX-RAY14和MIMIC CHEST XRAY JPG。结果表明，这两种模型的5种病理学的内部性能优于外部性能。此外，在培训阶段，我们将模型暴露于不同数据集混合的策略有助于提高外部数据集的模型性能。
Deep Convolutional Neural Networks (DCNNs) have attracted extensive attention and been applied in many areas, including medical image analysis and clinical diagnosis. One major challenge is to conceive a DCNN model with remarkable performance on both internal and external data. We demonstrate that DCNNs may not generalize to new data, but increasing the quality and heterogeneity of the training data helps to improve the generalizibility factor. We use InceptionResNetV2 and DenseNet121 architectures to predict the risk of 5 common chest pathologies. The experiments were conducted on three publicly available databases: CheXpert, ChestX-ray14, and MIMIC Chest Xray JPG. The results show the internal performance of each of the 5 pathologies outperformed external performance on both of the models. Moreover, our strategy of exposing the models to a mix of different datasets during the training phase helps to improve model performance on the external dataset.
