  Head CT is one of the most commonly performed imaging studied in the
Emergency Department setting and Intracranial hemorrhage (ICH) is among the
most critical and timesensitive findings to be detected on Head CT. We present
BloodNet, a deep learning architecture designed for optimal triaging of Head
CTs, with the goal of decreasing the time from CT acquisition to accurate ICH
detection. The BloodNet architecture incorporates dependency between the
otherwise independent tasks of segmentation and classification, achieving
improved classification results. AUCs of 0.9493 and 0.9566 are reported on held
out positive-enriched and randomly sampled sets comprised of over 1400 studies
acquired from over 10 different hospitals. These results are comparable to
previously reported results with smaller number of tagged studies.
