  Fully annotated large-scale medical image datasets are highly valuable.
However, because labeling medical images is tedious and requires specialized
knowledge, the large-scale datasets available often have missing annotation
issues. For instance, DeepLesion, a large-scale CT image dataset with labels
for various kinds of lesions, is reported to have a missing annotation rate of
50\%. Directly training a lesion detector on it would suffer from false
negative supervision caused by unannotated lesions. To address this issue,
previous works have used sophisticated multi-stage strategies to switch between
lesion mining and detector training. In this work, we present a novel
end-to-end framework for mining unlabeled lesions while simultaneously training
the detector. Our framework follows the teacher-student paradigm. In each
iteration, the teacher model infers the input data and creates a set of
predictions. High-confidence predictions are combined with partially-labeled
ground truth for training the student model. On the DeepLesion dataset, using
the original partially labeled training set, our model can outperform all other
more complicated methods and surpass the previous best method by 2.3\% on
average sensitivity and 2.7\% on average precision, achieving state-of-the-art
universal lesion detection results.
