Lack of interpretability of deep convolutional neural networks (DCNN) is a well-known problem particularly in the medical domain as clinicians want trustworthy automated decisions. One way to improve trust is to demonstrate the localisation of feature representations with respect to expert labeled regions of interest. In this work, we investigate the localisation of features learned via two varied learning paradigms and demonstrate the superiority of one learning approach with respect to localisation. Our analysis on medical and natural datasets show that the traditional end-to-end (E2E) learning strategy has a limited ability to localise discriminative features across multiple network layers. We show that a layer-wise learning strategy, namely cascade learning (CL), results in more localised features. Considering localisation accuracy, we not only show that CL outperforms E2E but that it is a promising method of predicting regions. On the YOLO object detection framework, our best result shows that CL outperforms the E2E scheme by $2\%$ in mAP.

翻译：深度卷积神经网络（DCNN）缺乏可解释性是一个公认的问题，尤其在医学领域，因为临床医生需要可信的自动化决策。提升信任度的一种方法是展示特征表示相对于专家标注的感兴趣区域的定位能力。本研究探讨了两种不同学习范式下所学特征的定位能力，并证明了其中一种学习范式在定位方面的优越性。我们在医学和自然数据集上的分析表明，传统的端到端（E2E）学习策略在多层网络中定位判别性特征的能力有限。而逐层学习策略，即级联学习（CL），能够产生更集中定位的特征。在定位准确度方面，我们不仅展示了CL优于E2E，还发现它是一种有前景的区域预测方法。在YOLO目标检测框架上，我们的最佳结果显示，CL在mAP上比E2E方案高出$2\%$。