Exploring the influence of attention for whole-image mammogram classification

Abstract

Attention is an important component of modern Convolutional Neural Networks (CNNs) that has been shown to improve baseline model performance for a wide variety of tasks. Attention has shown specific promise in the classification and segmentation of mammograms, but we have a limited understanding of why attention improves performance in these domains. In this paper, we present a robust comparison of different combinations of baseline models and attention methods at two resolutions for whole mammogram classification of masses and calcifications. We find that attention generally helps to improve baseline model performance. However, the extent of improvement is governed by a combination of model architecture and the statistical characteristics of the data. Specifically, we show that high amounts of pooling and model complexity may result in decreased performance for data with high variability. To better understand the effect of attention on mammogram classification, we used LayerCAM, a hierarchical Class Activation Map (CAM) approach, to visualize where the network pays attention in the input image. This research provides statistical evidence that attention can improve the correlation between model performance and LayerCAM activation in the region of interest (ROI). However, these correlations are weak and variable, indicating that improvements in model performance due to attention are not necessarily caused by increased model activation near the ROI. Overall, our work provides novel insights to help guide future efforts in incorporating attention-based mechanisms for mammogram classification.