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.

通过两种不同的学习模式来探究通过特征定位的方式学习到的特征，在医学和自然数据集上的分析表明了层级学习策略的优越性，本研究显示级联学习是一种有前景的区域预测方法，并在 YOLO 目标检测框架上显示出比端到端方案提升了 2% 的 mAP。