College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China; School of Computer Science and Engineering, University of New South Wales, Sydney, Australia.
College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
Trends Parasitol. 2024 Jul;40(7):633-646. doi: 10.1016/j.pt.2024.05.005. Epub 2024 May 31.
Microscopy image analysis plays a pivotal role in parasitology research. Deep learning (DL), a subset of artificial intelligence (AI), has garnered significant attention. However, traditional DL-based methods for general purposes are data-driven, often lacking explainability due to their black-box nature and sparse instructional resources. To address these challenges, this article presents a comprehensive review of recent advancements in knowledge-integrated DL models tailored for microscopy image analysis in parasitology. The massive amounts of human expert knowledge from parasitologists can enhance the accuracy and explainability of AI-driven decisions. It is expected that the adoption of knowledge-integrated DL models will open up a wide range of applications in the field of parasitology.
显微镜图像分析在寄生虫学研究中起着关键作用。深度学习（DL）作为人工智能（AI）的一个分支，受到了广泛关注。然而，传统的基于深度学习的通用方法是数据驱动的，由于其黑盒性质和稀缺的教学资源，往往缺乏可解释性。针对这些挑战，本文全面回顾了寄生虫学显微镜图像分析中知识集成深度学习模型的最新进展。寄生虫学家的大量人类专业知识可以提高 AI 驱动决策的准确性和可解释性。预计知识集成的深度学习模型的采用将为寄生虫学领域开辟广泛的应用前景。
