Recent advances in deep learning led to several algorithms for the accurate diagnosis of pneumonia from chest X-rays. However, these models require large training medical datasets, which are sparse, isolated, and generally private. Furthermore, these models in medical imaging are known to over-fit to a particular data domain source, i.e., these algorithms do not conserve the same accuracy when tested on a dataset from another medical center, mainly due to image distribution discrepancies. In this work, a domain adaptation and classification technique is proposed to overcome the over-fit challenges on a small dataset. This method uses a private-small dataset (target domain), a public-large labeled dataset from another medical center (source domain), and consists of three steps. First, it performs a data selection of the source domain’s most representative images based on similarity constraints through principal component analysis subspaces. Second, the selected samples from the source domain are fit to the target distribution through an image to image translation based on a cycle-generative adversarial network. Finally, the target train dataset and the adapted images from the source dataset are used within a convolutional neural network to explore different settings to adjust the layers and perform the classification of the target test dataset. It is shown that fine-tuning a few specific layers together with the selected-adapted images increases the sorting accuracy while reducing the trainable parameters. The proposed approach achieved a notable increase in the target dataset’s overall classification accuracy, reaching up to 97.78 % compared to 90.03 % by standard transfer learning.

中文翻译：

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CX-DaGAN：小型胸部 X 射线数据集上肺炎诊断的域适应


深度学习的最新进展催生了多种通过胸部 X 光检查准确诊断肺炎的算法。然而，这些模型需要大量的训练医学数据集，这些数据集稀疏、孤立且通常是私有的。此外，众所周知，医学成像中的这些模型会过度拟合特定的数据域源，即，当在来自另一个医疗中心的数据集上进行测试时，这些算法不能保持相同的精度，这主要是由于图像分布差异。在这项工作中，提出了一种域适应和分类技术来克服小数据集上的过度拟合挑战。该方法使用私有小型数据集（目标域）、来自另一个医疗中心的公共大型标记数据集（源域），并且由三个步骤组成。首先，它通过主成分分析子空间基于相似性约束对源域最具代表性的图像进行数据选择。其次，通过基于循环生成对抗网络的图像到图像转换，从源域中选择的样本适合目标分布。最后，目标训练数据集和来自源数据集的适应图像在卷积神经网络中使用，以探索不同的设置来调整层并执行目标测试数据集的分类。结果表明，对一些特定层与选定的适应图像一起进行微调可以提高排序精度，同时减少可训练参数。所提出的方法显着提高了目标数据集的整体分类精度，达到 97.78%，而标准迁移学习的分类精度为 90.03%。