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Investigation of cardinality classification for bacterial colony counting using explainable artificial intelligence

arXiv cs.CV / 4/23/2026

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Key Points

  • The paper studies why MicrobiaNet struggles to distinguish bacterial colony counts of three or more individuals, building on prior observations of degraded performance beyond that range.
  • Using explainable AI (XAI) analysis, the authors show that data properties—specifically high visual similarity between classes—are a key factor limiting cardinality classification accuracy.
  • The findings revise earlier assumptions about MicrobiaNet by attributing performance limits more to inter-class visual resemblance than to inherent model flaws alone.
  • The authors suggest future research should emphasize approaches that explicitly model visual similarity or shift toward density estimation methods for colony counting.
  • The work also points to broader lessons for neural network classifiers trained on imbalanced datasets, where confusable classes can cap achievable performance.

Abstract

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.

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