Advancements in digital pathology and computing resources have made a significant impact
in the field of computational pathology for breast cancer diagnosis and treatment. However …

A crucial element in the diagnosis of breast cancer is the utilization of a classification method
that is efficient, lightweight, and precise. Convolutional neural networks (CNNs) have …

Breast cancer, a leading cause of female mortality worldwide, poses a significant health
challenge. Recent advancements in deep learning techniques have revolutionized breast …

The objective of this study is to perform a comprehensive bibliometric analysis of the existing
literature on breast cancer segmentation using deep learning techniques. Data for this …

Graph data is an important data type for representing the relationships between individuals,
and many research works are conducted based on graph data. In the real-world, graph data …

Deep learning (DL) has recently gained popularity in forecasting, detecting, categorizing,
and diagnosing for breast cancer with promising results. Developing a review paper to …

Accurate detection of invasive breast cancer (IC) can provide decision support to
pathologists as well as improve downstream computational analyses, where detection of IC …

This paper introduces a self-attention Vision Transformer model specifically developed for
classifying breast cancer in histology images. We examine various training strategies and …

Breast tumor diagnosis has seen widespread use of computer-aided techniques. Machine
learning techniques can benefit doctors in making diagnosis decisions. One of the most …

We propose a self-attention Vision Transformer (ViT) model tailored for breast cancer
histology image classification. The proposed architecture uses a stack of transformer layers …