School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
The Sixth People's Hospital of Chengdu, Chengdu 610051, China.
J Healthc Eng. 2022 Feb 27;2022:1929371. doi: 10.1155/2022/1929371. eCollection 2022.
Vaginitis is a gynecological disease affecting the health of millions of women all over the world. The traditional diagnosis of vaginitis is based on manual microscopy, which is time-consuming and tedious. The deep learning method offers a fast and reliable solution for an automatic early diagnosis of vaginitis. However, deep neural networks require massive well-annotated data. Manual annotation of microscopic images is highly cost extensive because it not only is a time-consuming process but also needs highly trained people (doctors, pathologists, or technicians). Most existing active learning approaches are not applicable in microscopic images due to the nature of complex backgrounds and numerous formed elements. To address the problem of high cost of labeling microscopic images, we present a data-efficient framework for the identification of vaginitis based on transfer learning and active learning strategies. The proposed informative sample selection strategy selected the minimal training subset, and then the pretrained convolutional neural network (CNN) was fine-tuned on the selected subset. The experiment results show that the proposed pipeline can save 37.5% annotation cost while maintaining competitive performance. The proposed promising novel framework can significantly save the annotation cost and has the potential of extending widely to other microscopic imaging applications, such as blood microscopic image analysis.
阴道炎是一种影响全球数以百万计女性健康的妇科疾病。传统的阴道炎诊断基于人工显微镜检查，这种方法既耗时又繁琐。深度学习方法为阴道炎的自动早期诊断提供了一种快速可靠的解决方案。然而，深度神经网络需要大量的、标注良好的数据。对显微镜图像进行手动标注非常耗费人力和财力，因为这不仅是一个耗时的过程，还需要高度训练有素的人员（医生、病理学家或技术员）。由于复杂的背景和大量的形态学元素，大多数现有的主动学习方法不适用于显微镜图像。为了解决显微镜图像标注成本高的问题，我们提出了一种基于迁移学习和主动学习策略的阴道炎识别数据高效框架。所提出的信息样本选择策略选择了最小的训练子集，然后在所选子集中对预训练的卷积神经网络（CNN）进行微调。实验结果表明，所提出的方法可以在保持竞争性能的同时节省 37.5%的标注成本。所提出的有前景的新颖框架可以显著节省标注成本，并有可能广泛应用于其他显微镜成像应用，如血液显微镜图像分析。
