论文标题
使用深度学习的分辨率增强胎盘组织学图像
Resolution enhancement of placenta histological images using deep learning
论文作者
论文摘要
在这项研究中，已经开发了一种方法来改善组织学人胎盘图像的分辨率。为此，已经收集了一系列配对的高分辨率图像，以训练深层神经网络模型，该模型可以预测改善输入图像分辨率所需的图像残差。 U-NET神经网络模型的修改版本已量身定制，以找到低分辨率和残留图像之间的关系。在1000张图像的增强数据集上训练了900个时期后，用于预测320张测试图像的相对平均平方误差为0.003。所提出的方法不仅改善了细胞边缘处低分辨率图像的对比度，而且添加了模仿胎盘绒毛空间的高分辨率图像的关键细节和纹理。
In this study, a method has been developed to improve the resolution of histological human placenta images. For this purpose, a paired series of high- and low-resolution images have been collected to train a deep neural network model that can predict image residuals required to improve the resolution of the input images. A modified version of the U-net neural network model has been tailored to find the relationship between the low resolution and residual images. After training for 900 epochs on an augmented dataset of 1000 images, the relative mean squared error of 0.003 is achieved for the prediction of 320 test images. The proposed method has not only improved the contrast of the low-resolution images at the edges of cells but added critical details and textures that mimic high-resolution images of placenta villous space.
