Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks do not provide explanations for their predictions, which can lead to mistrust and suboptimal human-ML collaboration. Post-hoc explanation techniques, which are widely used in practice, have been shown to suffer from severe conceptual problems. Furthermore, as we show in this paper, current explanation techniques do not perform adequately in the multi-label scenario, in which multiple medical findings may co-occur in a single image. We propose Attri-Net, an inherently interpretable model for multi-label classification. Attri-Net is a powerful classifier that provides transparent, trustworthy, and human-understandable explanations. The model first generates class-specific attribution maps based on counterfactuals to identify which image regions correspond to certain medical findings. Then a simple logistic regression classifier is used to make predictions based solely on these attribution maps. We compare Attri-Net to five post-hoc explanation techniques and one inherently interpretable classifier on three chest X-ray datasets. We find that Attri-Net produces high-quality multi-label explanations consistent with clinical knowledge and has comparable classification performance to state-of-the-art classification models.

翻译：可解释性对于医疗图像分析等高安全性应用领域的机器学习算法至关重要。然而，高性能的黑盒神经网络无法为其预测提供解释，这可能导致信任缺失及人机协作效果欠佳。实践中广泛使用的后验解释技术已被证明存在严重概念性问题。此外，本文证实现有解释技术在多标签场景（单张图像中可能同时存在多个医学发现）下表现不足。我们提出Attri-Net——一种具有内在可解释性的多标签分类模型。该模型作为强分类器，能够提供透明、可信且人类可理解的解释。Attri-Net首先基于反事实生成类别特异性归因图，识别图像区域与特定医学发现的对应关系；随后使用简单逻辑回归分类器仅依据这些归因图进行预测。我们在三个胸部X光数据集上将Attri-Net与五种后验解释技术及一种内在可解释分类器进行比较，发现Attri-Net能生成与临床知识一致的高质量多标签解释，且分类性能可与最先进的分类模型相媲美。