Providing Mass Context to a Pretrained Deep Convolutional Neural Network for Breast Mass Classification
Lovisa Montelius, George Rezkalla · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2019
Breast cancer is one of the most common cancers among women in the world, and the average error rate among radiologists during diagnosis is 30%. Computer-aided medical diagnosis aims to assist doctors by giving them a second opinion, thus decreasing the error rate. Convolutional neural networks (CNNs) have shown to be good for visual detection and recognition tasks, and have been explored in combination with transfer learning. However, the performance of a deep learning model does not only rely on the model itself, but on the nature of the dataset as well In breast cancer diagnosis, the area surrounding a mass provides useful context for diagnosis. In this study, we explore providing different amounts of context to the CNN model ResNet50, to see how it affects the model’s performance. We test masses with no additional context, twice the amount of original context and four times the amount of original context, using 10-fold cross-validation with ROC AUC and average precision (AP ) as our metrics. The results suggest that providing additional context does improve the model’s performance. However, giving two and four times the amount of context seems to give similar performance.