  Universal Lesion Detection (ULD) in computed tomography plays an essential
role in computer-aided diagnosis. Promising ULD results have been reported by
coarse-to-fine two-stage detection approaches, but such two-stage ULD methods
still suffer from issues like imbalance of positive v.s. negative anchors
during object proposal and insufficient supervision problem during localization
regression and classification of the region of interest (RoI) proposals. While
leveraging pseudo segmentation masks such as bounding map (BM) can reduce the
above issues to some degree, it is still an open problem to effectively handle
the diverse lesion shapes and sizes in ULD. In this paper, we propose a
BM-based conditional training for two-stage ULD, which can (i) reduce positive
vs. negative anchor imbalance via BM-based conditioning (BMC) mechanism for
anchor sampling instead of traditional IoU-based rule; and (ii) adaptively
compute size-adaptive BM (ABM) from lesion bounding box, which is used for
improving lesion localization accuracy via ABMsupervised segmentation.
Experiments with four state-of-the-art methods show that the proposed approach
can bring an almost free detection accuracy improvement without requiring
expensive lesion mask annotations.
