Label-free approaches are attractive in cytological imaging due to their flexibility and cost efficiency. They are supported by machine learning methods, which, despite the lack of labeling and the associated lower contrast, can classify cells with high accuracy where the human observer has little chance to discriminate cells. In order to better integrate these workflows into the clinical decision making process, this work investigates the calibration of confidence estimation for the automated classification of leukocytes. In addition, different visual explanation approaches are compared, which should bring machine decision making closer to professional healthcare applications. Furthermore, we were able to identify general detection patterns in neural networks and demonstrate the utility of the presented approaches in different scenarios of blood cell analysis.

翻译：无标记方法因其灵活性和成本效益在细胞学成像中备受关注。这类方法依托机器学习技术——尽管缺乏标记导致对比度较低，但模型能在人类观察者难以区分细胞的情况下实现高精度分类。为更好地将这些工作流程整合到临床决策过程中，本研究探讨了白细胞自动分类中置信度估计的校准方法。此外，本文比较了不同的视觉解释方法，旨在推动机器决策更贴近专业医疗应用场景。进一步地，我们成功识别出神经网络中的通用检测模式，并在血细胞分析的不同场景中验证了所提方法的实用价值。