Visual Correspondence-based Explanations Improve Convolutional Neural Networksfor Classificationof Mammograms
Year: 2025
Type: article
Abstract: Explainable Artificial Intelligence (XAI) classifications are increasingyimportant in convolutional neural networks for the classification of mammograms. In this paper, we propose a novel architecture of Visual Correspondence Based Explanations that improve Convolutional Neural Networks (cnns) for Classification of Mammograms of self-interpretable image classifiers that first explain, and then predict by harnessing the visual correspondences between a query breast cancer X-Ray image and exemplars. In the evaluation of our proposed models, the k-nearest neighbor (knn) classifier improves upon ResNet-18 on Breast Cancer datasets.
Authors: Ha Manh Toan, Nguyen Hoang Phuong
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414097359