Breast Cancer Histopathological Image Classification

Breast cancer histopathological image classification uses computational methods to automatically analyze microscopic images of breast tissue, aiding in cancer diagnosis and grading. Current research focuses on improving classification accuracy and generalizability using deep learning architectures like Vision Transformers (ViTs), ResNets, and ensemble methods that combine multiple models or color spaces for feature extraction. These advancements aim to improve diagnostic efficiency, reduce reliance on pathologist expertise, and potentially lead to more personalized treatment strategies. Challenges remain in addressing data imbalance, stain variability, and the need for robust validation across diverse datasets.