论文标题
移动显微镜的深度学习框架
Deep learning Framework for Mobile Microscopy
论文作者
论文摘要
移动显微镜是一项有前途的技术，可以协助和加速疾病诊断，其广泛的采用受到收购图像的中等质量的阻碍。尽管已经出现了某些配对的图像翻译和移动显微镜的超分辨率方法，但是在高通量设置中自动化它所必需的一系列基本挑战，仍在等待解决。当使用移动设备录制数据时，诸如焦点/焦点外分类，快速扫描Deblurring，Focus-stacking等问题都具有特定的特点。在这项工作中，我们渴望通过连接一组故意调谐到移动显微镜的方法来创建一条全面的管道：（1）用于稳定的稳定的焦点内 /过量焦点分类的CNN模型，（2）用于图像DeBlurring的修改DeBlergan架构，（3）Fusegan模型，用于合并来自多个图像的零件的Fusegan模型。我们讨论针对专业临床显微镜开发的现有解决方案的局限性，提出相应的改进，并与其他最先进的移动分析解决方案进行比较。
Mobile microscopy is a promising technology to assist and to accelerate disease diagnostics, with its widespread adoption being hindered by the mediocre quality of acquired images. Although some paired image translation and super-resolution approaches for mobile microscopy have emerged, a set of essential challenges, necessary for automating it in a high-throughput setting, still await to be addressed. The issues like in-focus/out-of-focus classification, fast scanning deblurring, focus-stacking, etc. -- all have specific peculiarities when the data are recorded using a mobile device. In this work, we aspire to create a comprehensive pipeline by connecting a set of methods purposely tuned to mobile microscopy: (1) a CNN model for stable in-focus / out-of-focus classification, (2) modified DeblurGAN architecture for image deblurring, (3) FuseGAN model for combining in-focus parts from multiple images to boost the detail. We discuss the limitations of the existing solutions developed for professional clinical microscopes, propose corresponding improvements, and compare to the other state-of-the-art mobile analytics solutions.
