Deep learning

Physics

Internal medicine

Pattern recognition (psychology)

Overfitting

Preprocessor

Contextual image classification

Gene

Entropy (arrow of time)

Image (mathematics)

Cancer

Machine learning

Artificial intelligence

Chemistry

Softmax function

Digital pathology

Computer science

Breast cancer

Quantum mechanics

Artificial neural network

Biochemistry

Robustness (evolution)

Medicine

Convolutional neural network

IEEE Journal of Biomedical and Health Informatics

Breast cancer is the most common female cancer in the world, and it poses a huge threat to women's health. There is currently promising research concerning its early diagnosis using deep learning methodologies. However, some commonly used Convolutional Neural Network (CNN) and their variations, such as AlexNet, VGGNet, GoogleNet and so on, are prone to overfitting in breast cancer classification, due to both small-scale breast pathology image datasets and overconfident softmax-cross-entropy loss. To alleviate the overfitting issue for better classification accuracy, we propose a novel framework for breast pathology classification, called the AlexNet-BC model. The model is pre-trained using the ImageNet dataset and fine-tuned using an augmented dataset. We also devise an improved cross-entropy loss function to penalize overconfident low-entropy output distributions and make the predictions suitable for uniform distributions. The proposed approach is then validated through a series of comparative experiments on BreaKHis, IDC and UCSB datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods at different magnifications. Its strong robustness and generalization capabilities make it suitable for histopathology clinical computer-aided diagnosis systems.

A Deep Learning Method for Breast Cancer Classification in the Pathology Images