A knowledge-integrated deep learning framework for cellular image analysis in parasite microbiology

Cellular image analysis is an important method for microbiologists to identify and study microbes. Here, we present a knowledge-integrated deep learning framework for cellular image analysis, using three tasks as examples: classification, detection, and reconstruction. Alongside thorough descriptions of different models and datasets, we describe steps for computing environment setup, knowledge representation, data pre-processing, and training and tuning. We then detail evaluation and visualization. For complete details on the use and execution of this protocol, please refer to Li et al. (2021),1 Jiang et al. (2020),2 and Zhang et al. (2022).3

Automated summary

Cellular image analysis is a crucial method employed by microbiologists for the identification and study of microbes. The article presents a knowledge-integrated deep learning framework for cellular image analysis, focusing on classification, detection, and reconstruction tasks. It provides comprehensive information on various models, datasets, computing environment setup, knowledge representation, data pre-processing, and training and tuning, as well as evaluation and visualization techniques.