The resulting system achieves a sensitivity for malignant findings of 0.99 with only 4.8 false positive markers per image. We replace the pixel-wise L2 norm with a weak-supervision loss designed to achieve high sensitivity, asymmetrically penalizing false positives and false negatives while softening the noise of the loose bounding boxes by permitting a tolerance in misaligned predictions. Building upon work from the Hourglass architecture, we train a model that produces segmentation- like images with high spatial resolution, with the aim of producing 2D Gaussian blobs centered on ground-truth boxes. In this work, we leverage a large set of FFDM images with loose bounding boxes of mammographically significant findings to train a deep learning detector with extreme sensitivity.
#IMAGING COMPUTER SOFTWARE#
Although commercial computer aided detection (CADe) software has been available to radiologists for decades, it has failed to improve the interpretation of full-field digital mammography (FFDM) images due to its low sensitivity over the spectrum of findings. Early detection of breast cancer through screening mammography yields a 20-35% increase in survival rate however, there are not enough radiologists to serve the growing population of women seeking screening mammography.
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