Digital mammography screening is an important exam for the early detection of breast
cancer and reduction in mortality. False positives leading to high recall rates, however,
results in unnecessary negative consequences to patients and health care systems.
In order to better aid radiologists, computer-aided tools can be utilized to improve
distinction between image classifications and thus potentially reduce false recalls.
The emergence of deep learning has shown promising results in the area of biomedical
imaging data analysis. This study aimed to investigate deep learning and transfer
learning methods that can improve digital mammography classification performance.
In particular, we evaluated the effect of pre-training deep learning models with other
imaging datasets in order to boost classification performance on a digital mammography
dataset Two types of datasets were used for pre-training: (1) a digitized film mammography
dataset, and (2) a very large non-medical imaging dataset. By using either of these
datasets to pre-train the network initially, and then fine-tuning with the digital
mammography dataset, we found an increase in overall classification performance in
comparison to a model without pre-training, with the very large non-medical dataset
performing the best in improving the classification accuracy.
