用可解释AI发现细菌菌落计数难点在于视觉相似性
Investigation of cardinality classification for bacterial colony counting using explainable artificial intelligence
- 用可解释AI分析MicrobiaNet模型的决策过程
- 发现三及以上菌落因视觉相似导致识别率低
- 适合关注生物图像分析与模型可解释性的研究者
自动细菌菌落计数在现代生物实验室中极具价值，可消除人工计数负担。此前研究发现，当前性能最优的菌落基数分类模型MicrobiaNet难以区分三个及以上个体的菌落。然而，这一问题是否源于数据特性与模型固有局限尚不明确。通过运用可解释人工智能（XAI）对MicrobiaNet进行分析，我们揭示了数据属性如何制约基数分类性能。结果表明，不同类别间高度的视觉相似性是阻碍性能提升的关键因素，这修正了以往对MicrobiaNet局限性的理解。该发现提示未来工作应聚焦于显式建模视觉相似性的模型，或探索密度估计方法，对训练于不平衡数据集的神经网络分类器具有广泛启示。
原文摘要 · Abstract (English)
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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