Abstract
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.
Abstract (translated)
深度卷积神经网络（DCNN）的可解释性不足是一个已知的问题，尤其是在医学领域，因为临床医生希望得到可信赖的自动决策。提高信任的一种方法是证明特征表示与专家标注的感兴趣区域局部相关。在这项工作中，我们研究了通过两种不同的学习范式学习到的特征的局部化，并证明了在局部化方面，一种学习方法比另一种更优越。我们对医学和自然数据集的分析表明，传统的端到端（E2E）学习策略在多层网络中定位有区别的特征方面有限。我们证明了级联学习（CL）策略导致了更局部的特征。考虑到局部化精度，我们不仅证明了CL优于E2E，而且它是一种有前景的预测区域的方法。在YOLO目标检测框架上，我们的最佳结果表明，CL在mAP方面超越了E2E方案2%。
URL
https://arxiv.org/abs/2311.12704
