  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. 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. 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. 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. We replace the pixel-wise $L_2$ 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. The
resulting system achieves a sensitivity for malignant findings of 0.99 with
only 4.8 false positive markers per image. When utilized in a CADe system, this
model could enable a novel workflow where radiologists can focus their
attention with trust on only the locations proposed by the model, expediting
the interpretation process and bringing attention to potential findings that
could otherwise have been missed. Due to its nearly perfect sensitivity, the
proposed detector can also be used as a high-performance proposal generator in
two-stage detection systems.
