Transfer Learning in Breast Mass Detection on the OMI-DB Dataset: A Preliminary Study

Early screening for breast cancer is an effective tool to detect tumors and decrease mortality among women. However, COVID restrictions made screening difficult in recent years due to a decrease in screening tests, reduction of routine procedures, and their delay. This preliminary study aimed to investigate mass detection in a large-scale OMI-DB dataset with three Transfer Learning settings in the early screening. We considered a subset of the OMI-DB dataset consisting of 6,000 cases, where we extracted 3,525 images with masses of Hologic Inc. manufacturer. This paper proposes to use the RetinaNet model with ResNet50 backbone to detect tumors in Full-Field Digital Mammograms. The model was initialized with ImageNet weights, COCO weights, and from scratch. We applied True Positive Rate at False Positive per Image evaluation metric with Free-Response Receiver Operating Characteristic curve to visualize the distributions of the detections. The proposed framework obtained 0.93 TPR at 0.84 FPPI with COCO weights initialization. ImageNet weights gave comparable results of 0.93 at 0.84 FPPI and from scratch demonstrated 0.84 at 0.84 FPPI.