Department of Electronic Engineering, Kwangwoon University, Seoul 01897, Korea.
Molpaxbio, Daejeon 34047, Korea.
Sensors (Basel). 2022 May 23;22(10):3960. doi: 10.3390/s22103960.
Digital pathology analysis using deep learning has been the subject of several studies. As with other medical data, pathological data are not easily obtained. Because deep learning-based image analysis requires large amounts of data, augmentation techniques are used to increase the size of pathological datasets. This study proposes a novel method for synthesizing brain tumor pathology data using a generative model. For image synthesis, we used embedding features extracted from a segmentation module in a general generative model. We also introduce a simple solution for training a segmentation model in an environment in which the masked label of the training dataset is not supplied. As a result of this experiment, the proposed method did not make great progress in quantitative metrics but showed improved results in the confusion rate of more than 70 subjects and the quality of the visual output.
深度学习在数字病理学分析中的应用已经成为了多个研究的主题。与其他医学数据一样，病理学数据不易获取。由于基于深度学习的图像分析需要大量的数据，因此需要使用扩充技术来增加病理学数据集的规模。本研究提出了一种使用生成模型合成脑肿瘤病理学数据的新方法。对于图像合成，我们使用从一般生成模型中的分割模块中提取的嵌入特征。我们还引入了一种简单的解决方案，用于在训练数据集的掩模标签未提供的环境中训练分割模型。通过这项实验，所提出的方法在定量指标上并没有取得很大的进展，但在超过 70 个主体的混淆率和视觉输出的质量方面显示出了改进的结果。
