Sample-efficient framework for breast lesion detection in digital breast tomosynthesis: preliminary analysis on its generalizability

The purpose of this study was to test the generalizability of our sample-efficient lesion detection framework for biopsy-proven breast lesions detection on digital breast Tomosynthesis (DBT). We developed a sample-efficient breast lesion detection framework using a set of limited biopsied DBT lesions. Instead of using large in-house lesion dataset that only a few can access, we utilized non-biopsied false positive findings to augment the limited training set. We applied our framework on open-source single and multi-stage Convolutional Neural Network based object detectors to show the generalizability of our framework. Then, we combined different detector models using ensemble approach to further improve the detection performance. Using a challenge validation set, we achieved detection performance (a mean sensitivity of 0.84 FPs per DBT volume and sensitivity of 0.80 at 2 false positives per image) close to one of top-ranking algorithms in the DBT lesion detection challenge which augmented the training set with a large in-house mammogram dataset.