Data Augmentation for Detection of Architectural Distortion in Digital Mammography using Deep Learning Approach
This addresses the challenge of subtle breast cancer signs often missed by radiologists, but it is incremental as it applies existing deep learning methods to a new medical imaging task.
The paper tackled the problem of detecting Architectural Distortion in digital mammography for early breast cancer detection by proposing a data augmentation approach to train a Convolutional Neural Network with limited data, achieving an AUC of 0.74.
Early detection of breast cancer can increase treatment efficiency. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. To train a Convolutional Neural Network (CNN), which is a deep neural architecture, is necessary a huge amount of data. To overcome this problem, this paper proposes a data augmentation approach applied to clinical image dataset to properly train a CNN. Results using receiver operating characteristic analysis showed that with a very limited dataset we could train a CNN to detect AD in digital mammography with area under the curve (AUC = 0.74).