Example testing images for PFU detection neural network

This is the example testing images used for the paper of "Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning". The data has been compressed into eight zip files from .001 to .008. To access the data, please follow these steps: 1) Download all eight zipped files. 2) Once the download is complete, click on any one of the zipped files to start the extraction process. 3) The extracted files will automatically be organized into one folder. After extraction, there are three subfolders inside it: 1) Network input: it contained 4 holographic phase images (.mat files) at 12h, 13h, 14h and 15h of incubation for an example postive well and an example negative well. 2) Network output: It contains the network output image (PFU probability map) fot the example postive well and the example negative well. 3) Detection result: It contains the final binary detection result images after thresholding fot the example postive well and the example negative well.本数据集为论文《通过无透镜全息术与深度学习快速无染色定量病毒噬斑》（"Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning"）所用的示例测试图像。数据集已被压缩为从.001至.008的八个压缩包。 如需获取该数据，请遵循以下步骤： 1）下载全部八个压缩包； 2）下载完成后，点击任意一个压缩包即可启动解压流程； 3）解压后的文件将自动整合为一个文件夹。 解压完成后，该文件夹内包含三个子文件夹： 1）网络输入（Network input）：包含1个阳性示例孔与1个阴性示例孔在孵育12h、13h、14h、15h时的4幅全息相位图像（.mat格式文件）； 2）网络输出（Network output）：包含阳性示例孔与阴性示例孔的网络输出图像——噬斑形成单位（PFU，plaque-forming unit）概率图； 3）检测结果（Detection result）：包含对阳性示例孔与阴性示例孔进行阈值处理后得到的最终二值化检测结果图像。