Computational mammography using deep neural networks

Abstract

Automatic tissue classification from medical images is an important step in pathology detection and diagnosis. Here, we deal with mammography images and present a novel supervised deep learning-based framework for region classification into semantically coherent tissues. The proposed method uses Convolutional Neural Network (CNN) to learn discriminative features automatically. We overcome the difficulty involved in a medium-size database by training the CNN in an overlapping patch-wise manner. In order to accelerate the pixel-wise automatic class prediction, we use convolutional layers instead of the classical fully connected layers. This approach results in significantly faster computation, while preserving the classification accuracy. The proposed method was tested on annotated mammography images and demonstrates promising image segmentation and tissue classification results.

Keywords:

• Digital mammography
• multi-region segmentation
• deep neural networks

Acknowledgements

We would like to thank Menashe-Meni Amran for his help with the image annotation.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Advanced European Community’s FP7-ERC program [grant number 267414].