  Hip and pelvic fractures are serious injuries with life-threatening
complications. However, diagnostic errors of fractures in pelvic X-rays (PXRs)
are very common, driving the demand for computer-aided diagnosis (CAD)
solutions. A major challenge lies in the fact that fractures are localized
patterns that require localized analyses. Unfortunately, the PXRs residing in
hospital picture archiving and communication system do not typically specify
region of interests. In this paper, we propose a two-stage hip and pelvic
fracture detection method that executes localized fracture classification using
weakly supervised ROI mining. The first stage uses a large capacity
fully-convolutional network, i.e., deep with high levels of abstraction, in a
multiple instance learning setting to automatically mine probable true positive
and definite hard negative ROIs from the whole PXR in the training data. The
second stage trains a smaller capacity model, i.e., shallower and more
generalizable, with the mined ROIs to perform localized analyses to classify
fractures. During inference, our method detects hip and pelvic fractures in one
pass by chaining the probability outputs of the two stages together. We
evaluate our method on 4 410 PXRs, reporting an area under the ROC curve value
of 0.975, the highest among state-of-the-art fracture detection methods.
Moreover, we show that our two-stage approach can perform comparably to human
physicians (even outperforming emergency physicians and surgeons), in a
preliminary reader study of 23 readers.
