Breast Cancer Diagnosis with Transfer Learning and Global Pooling

Understand

Breast cancer is one of the most common causes of cancer-related death in women worldwide.

• Early and accurate diagnosis of breast cancer may significantly increase the survival rate of patients.
• In this study, we aim to develop a fully automatic, deep learning-based, method using descriptor features extracted by Deep Convolutional Neural Network (DCNN) models and pooling operation for the classification of hematoxylin and eosin stain (H&E) histological breast cancer images provided as a part of the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology (BACH) Images.
• Different data augmentation methods are applied to optimize the DCNN performance.