Optimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification
The study investigated methods to improve breast cancer detection using deep learning on mammograms. It compared different training strategies, resolution reduction techniques, and multi-view classification approaches across various datasets. The findings suggest that combining two views is superior and that patch-based pretraining helps when applied to natural images.