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.
Sarah S. Aboutalib, Aly A. Mohamed, Margarita L. Zuley, Wendie A. Berg, Yahong Luo, and Shandong Wu, "Do pre-trained deep learning models improve computer-aided classification of digital mammograms?
," Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057523 (Presented at SPIE Medical Imaging: February 16, 2018; Published: 27 February 2018); https://doi.org/10.1117/12.2293777.
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