Zhang Chi, Jiang Hao, Jiang Hanlin, Xi Hui, Chen Baodong, Liu Yubing, Juhas Mario, Li Junyi, Zhang Yang
College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
Department of Neurosurgery, Shenzhen Hospital of Peking University, Shenzhen, Guangdong, China.
Comput Struct Biotechnol J. 2022 Feb 11;20:1036-1043. doi: 10.1016/j.csbj.2022.02.005. eCollection 2022.
The infectious and parasitic diseases represent a major threat to public health and are among the main causes of morbidity and mortality. The complex and divergent life cycles of parasites present major difficulties associated with the diagnosis of these organisms by microscopic examination. Deep learning has shown extraordinary performance in biomedical image analysis including various parasites diagnosis in the past few years. Here we summarize advances of deep learning in the field of protozoan parasites microscopic examination, focusing on publicly available microscopic image datasets of protozoan parasites. In the end, we summarize the challenges and future trends, which deep learning faces in protozoan parasite diagnosis.
传染病和寄生虫病对公众健康构成重大威胁，是发病和死亡的主要原因之一。寄生虫复杂多样的生命周期给通过显微镜检查诊断这些生物带来了重大困难。在过去几年中，深度学习在生物医学图像分析中表现出非凡的性能，包括各种寄生虫的诊断。在此，我们总结深度学习在原生动物寄生虫显微镜检查领域的进展，重点关注公开可用的原生动物寄生虫显微镜图像数据集。最后，我们总结了深度学习在原生动物寄生虫诊断中面临的挑战和未来趋势。
