Exploring Regions of Interest: Visualizing Histological Image
  Classification for Breast Cancer using Deep Learning
Imane Nedjar Mohammed Brahimi Said Mahmoudi Khadidja Abi Ayad Mohammed Amine Chikh
Computer aided detection and diagnosis systems based on deep learning have
shown promising performance in breast cancer detection. However, there are
cases where the obtained results lack justification. In this study, our
objective is to highlight the regions of interest used by a convolutional
neural network (CNN) for classifying histological images as benign or
malignant. We compare these regions with the regions identified by
pathologists. To achieve this, we employed the VGG19 architecture and tested
three visualization methods: Gradient, LRP Z, and LRP Epsilon. Additionally, we
experimented with three pixel selection methods: Bins, K-means, and MeanShift.
Based on the results obtained, the Gradient visualization method and the
MeanShift selection method yielded satisfactory outcomes for visualizing the
images.