# Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning

arXiv: https://arxiv.org/abs/2305.20058 · HTML: https://mlsanity.com/paper/2305.20058/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification](https://mlsanity.com/paper/1701.06643/) (2017)
- [Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification](https://mlsanity.com/paper/1803.02544/) (2018)
- [Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/1808.02874/) (2018)
- [Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification](https://mlsanity.com/paper/1809.03851/) (2018)

## Built on by (most cited first, 1 of 1)

- [Breast Cancer Classification with Enhanced Interpretability: DALAResNet50 and DT Grad-CAM](https://mlsanity.com/paper/2308.13150/) (2023)

## Abstract (arXiv)

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.