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
5/31/2023
cs.CV

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Abstract

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.

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Cite this paper

@article{nedjar2023exploring,
  title  = {Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning},
  author = {Imane Nedjar and Mohammed Brahimi and Said Mahmoudi and Khadidja Abi Ayad and Mohammed Amine Chikh},
  year   = {2023},
  eprint = {2305.20058},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.20058v1}
}

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