论文标题
远处的域转移学习用于医学成像
Distant Domain Transfer Learning for Medical Imaging
论文作者
论文摘要
医疗图像处理是医学事物互联网（IOMT）领域中最重要的主题之一。最近，深度学习方法已经在医学图像任务上进行了最先进的表演。但是，传统的深度学习有两个主要缺点：1）培训数据不足，2）培训数据与测试数据之间的域不匹配。在本文中，我们提出了一种遥远的域转移学习（DDTL）方法，以进行医学图像分类。此外，我们将方法应用于最近的问题（冠状病毒诊断）。当前的几项研究表明，肺计算机断层扫描（CT）图像可用于快速准确的COVID-19诊断。但是，由于疾病的新颖性和许多隐私政策，标记良好的培训数据无法轻松获取。此外，提出的方法具有两个组成部分：减少大小的UNET分割模型和远处特征融合（DFF）分类模型。它与未经审查但重要的转移学习问题（称为遥远的域转移学习（DDTL））有关。即使域或任务完全不同，DDTL旨在进行有效的转移。在这项研究中，我们使用未标记的Office-31，Catech-256和Chest X射线图像数据集作为源数据开发了用于COVID-19的DDTL模型，以及一小部分Covid-19肺CT作为目标数据。这项研究的主要贡献：1）所提出的方法受益于从遥远领域收集的未标记数据，可容易访问，2）它可以有效地处理培训数据和测试数据之间的分布变化，3）它已达到96 \％的分类准确性，比“非转移”转移和8％的转移和8％的转移和8 \％的转移，并且分类的准确性高13 \％。
Medical image processing is one of the most important topics in the field of the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical image tasks. However, conventional deep learning have two main drawbacks: 1) insufficient training data and 2) the domain mismatch between the training data and the testing data. In this paper, we propose a distant domain transfer learning (DDTL) method for medical image classification. Moreover, we apply our methods to a recent issue (Coronavirus diagnose). Several current studies indicate that lung Computed Tomography (CT) images can be used for a fast and accurate COVID-19 diagnosis. However, the well-labeled training data cannot be easily accessed due to the novelty of the disease and a number of privacy policies. Moreover, the proposed method has two components: Reduced-size Unet Segmentation model and Distant Feature Fusion (DFF) classification model. It is related to a not well-investigated but important transfer learning problem, termed Distant Domain Transfer Learning (DDTL). DDTL aims to make efficient transfers even when the domains or the tasks are entirely different. In this study, we develop a DDTL model for COVID-19 diagnose using unlabeled Office-31, Catech-256, and chest X-ray image data sets as the source data, and a small set of COVID-19 lung CT as the target data. The main contributions of this study: 1) the proposed method benefits from unlabeled data collected from distant domains which can be easily accessed, 2) it can effectively handle the distribution shift between the training data and the testing data, 3) it has achieved 96\% classification accuracy, which is 13\% higher classification accuracy than "non-transfer" algorithms, and 8\% higher than existing transfer and distant transfer algorithms.
