Bacterial image analysis using multi-task deep learning approaches for clinical microscopy - Chin, Shuang Yee; Jian, Dong; Khairunnisa, Hasikin; Romano, Ngui; Lai, Khin Wee; Pauline Yeoh, Shan Qing; Xiang, Wu (Tim O'Reilly (via O'Reilly Media and O'Reilly AlphaTech Ventures) and SAGE publishing, 2024)
Background Bacterial image analysis plays a vital role in various fields, providing valuable information and insights for studying bacterial structural biology, diagnosing and treating infectious diseases caused by pathogenic bacteria, discovering and developing drugs that can combat bacterial infections, etc. As a result, it has prompted efforts to automate bacterial image analysis tasks. By automating analysis tasks and leveraging more advanced computational techniques, such as deep learning (DL) algorithms, bacterial image analysis can contribute to rapid, more accurate, efficient, reliable, and standardised analysis, leading to enhanced understanding, diagnosis, and control of bacterial-related phenomena.