Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
This work addresses the problem of improving diagnostic accuracy for breast cancer, which is crucial for early treatment and survival, but it appears incremental as it applies existing deep learning methods to a specific medical dataset.
The authors tackled breast cancer histology image classification using deep convolutional neural networks and gradient boosted trees, achieving 87.2% accuracy for 4-class classification and 93.8% accuracy with 97.3% AUC for carcinoma detection.
Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy. For 2-class classification task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018