Zhang Yang, Jiang Hao, Ye Taoyu, Juhas Mario
College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, China.
College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, China.
Trends Microbiol. 2021 Jul;29(7):569-572. doi: 10.1016/j.tim.2021.01.006. Epub 2021 Jan 30.
Despite tremendous recent interest, the application of deep learning in microbiology has still not reached its full potential. To tackle the challenges faced by human-operated microscopy, deep-learning-based methods have been proposed for microscopic image analysis of a wide range of microorganisms, including viruses, bacteria, fungi, and parasites. We believe that deep-learning technology-based systems will be on the front line of monitoring and investigation of microorganisms.
尽管近期受到了极大关注,但深度学习在微生物学中的应用仍未充分发挥其潜力。为应对人工显微镜操作所面临的挑战,已提出基于深度学习的方法用于对包括病毒、细菌、真菌和寄生虫在内的多种微生物进行显微图像分析。我们相信,基于深度学习技术的系统将处于微生物监测和研究的前沿。