Investigation of cardinality classification for bacterial colony counting using explainable artificial intelligence
This work addresses a specific challenge in automated bacterial colony counting for biological laboratories, offering incremental insights by clarifying data constraints rather than proposing a new method.
The study investigated why MicrobiaNet struggles with classifying bacterial colonies of three or more individuals, using explainable AI to reveal that high visual similarity across classes is the key performance bottleneck, revising prior assumptions about the model.
Automatic bacterial colony counting is a highly sought-after technology in modern biological laboratories because it eliminates manual counting effort. Previous work has observed that MicrobiaNet, currently the best-performing cardinality classification model for colony counting, has difficulty distinguishing colonies of three or more individuals. However, it is unclear if this is due to properties of the data together with inherent characteristics of the MicrobiaNet model. By analysing MicrobiaNet with explainable artificial intelligence (XAI), we demonstrate that XAI can provide insights into how data properties constrain cardinality classification performance in colony counting. Our results show that high visual similarity across classes is the key issue hindering further performance improvement, revising prior assertions about MicrobiaNet. These findings suggest future work should focus on models that explicitly incorporate visual similarity or explore density estimation approaches, with broader implications for neural network classifiers trained on imbalanced datasets.