  Chest X-ray (CXR) is the most typical medical image worldwide to examine
various thoracic diseases. Automatically localizing lesions from CXR is a
promising way to alleviate radiologists' daily reading burden. However, CXR
datasets often have numerous image-level annotations and scarce lesion-level
annotations, and more often, without annotations. Thus far, unifying different
supervision granularities to develop thoracic disease detection algorithms has
not been comprehensively addressed. In this paper, we present OXnet, the first
deep omni-supervised thoracic disease detection network to our best knowledge
that uses as much available supervision as possible for CXR diagnosis. Besides
fully supervised learning, to enable learning from weakly-annotated data, we
guide the information from a global classification branch to the lesion
localization branch by a dual attention alignment module. To further enhance
global information learning, we impose intra-class compactness and inter-class
separability with a global prototype alignment module. For unsupervised data
learning, we extend the focal loss to be its soft form to distill knowledge
from a teacher model. Extensive experiments show the proposed OXnet outperforms
competitive methods with significant margins. Further, we investigate
omni-supervision under various annotation granularities and corroborate OXnet
is a promising choice to mitigate the plight of annotation shortage for medical
image diagnosis.
