Would you trust physicians if they cannot explain their decisions to you? Medical diagnostics using machine learning gained enormously in importance within the last decade. However, without further enhancements many state-of-the-art machine learning methods are not suitable for medical application. The most important reasons are insufficient data set quality and the black-box behavior of machine learning algorithms such as Deep Learning models. Consequently, end-users cannot correct the model's decisions and the corresponding explanations. The latter is crucial for the trustworthiness of machine learning in the medical domain. The research field explainable interactive machine learning searches for methods that address both shortcomings. This paper extends the explainable and interactive CAIPI algorithm and provides an interface to simplify human-in-the-loop approaches for image classification. The interface enables the end-user (1) to investigate and (2) to correct the model's prediction and explanation, and (3) to influence the data set quality. After CAIPI optimization with only a single counterexample per iteration, the model achieves an accuracy of $97.48\%$ on the Medical MNIST and $95.02\%$ on the Fashion MNIST. This accuracy is approximately equal to state-of-the-art Deep Learning optimization procedures. Besides, CAIPI reduces the labeling effort by approximately $80\%$.

翻译：如果医生无法向您解释他们的决定,您会信任医生吗? 使用机器学习的医疗诊断在过去十年中变得非常重要。 但是,如果不进一步加强,许多最先进的机器学习方法不适于医疗应用。 最重要的原因是数据集质量不足以及深学习模型等机器学习算法的黑盒行为。 因此,最终用户无法纠正模型的决定和相应的解释。 后者对于医疗领域机器学习的可信度至关重要。 研究领域可以解释的互动机器学习搜索方法解决这两个缺陷。 本文扩展了可解释和互动的 CAIPI 算法,并提供了一个界面,以简化图像分类的“在行人”方法。 界面使终端用户(1) 能够调查和(2) 纠正模型的预测和解释,(3) 影响数据集的质量。 在CAIPI 优化后,仅用一个反比试,该模型在医疗MNIST上实现了97. 48 美元 的准确度,在Fashian CIP 上实现了9. 00.2 美元 美元 的精确度。 此精确度相当于CILI 的升级。