Deep learning methods provide state of the art performance for supervised learning based medical image analysis. However it is essential that trained models extract clinically relevant features for downstream tasks as, otherwise, shortcut learning and generalization issues can occur. Furthermore in the medical field, trustability and transparency of current deep learning systems is a much desired property. In this paper we propose an interpretability-guided inductive bias approach enforcing that learned features yield more distinctive and spatially consistent saliency maps for different class labels of trained models, leading to improved model performance. We achieve our objectives by incorporating a class-distinctiveness loss and a spatial-consistency regularization loss term. Experimental results for medical image classification and segmentation tasks show our proposed approach outperforms conventional methods, while yielding saliency maps in higher agreement with clinical experts. Additionally, we show how information from unlabeled images can be used to further boost performance. In summary, the proposed approach is modular, applicable to existing network architectures used for medical imaging applications, and yields improved learning rates, model robustness, and model interpretability.

深度学习方法为基于监督学习的医学图像分析提供了最先进的性能。然而，经过训练的模型必须为下游任务提取临床相关特征，否则可能会出现捷径学习和泛化问题。此外，在医学领域，当前深度学习系统的可信度和透明度是一个非常受欢迎的特性。在本文中，我们提出了一种可解释性引导的归纳偏差方法，该方法强制学习的特征为训练模型的不同类别标签产生更独特和空间一致的显着性图，从而提高模型性能。我们通过结合类区别性损失和空间一致性正则化损失项来实现我们的目标。医学图像分类和分割任务的实验结果表明，我们提出的方法优于传统方法，同时生成的显着图与临床专家的一致性更高。此外，我们展示了如何使用来自未标记图像的信息来进一步提高性能。总之，所提出的方法是模块化的，适用于用于医学成像应用的现有网络架构，并提高了学习率、模型鲁棒性和模型可解释性。