  Universal lesion detection (ULD) on computed tomography (CT) images is an
important but underdeveloped problem. Recently, deep learning-based approaches
have been proposed for ULD, aiming to learn representative features from
annotated CT data. However, the hunger for data of deep learning models and the
scarcity of medical annotation hinders these approaches to advance further. In
this paper, we propose to incorporate domain knowledge in clinical practice
into the model design of universal lesion detectors. Specifically, as
radiologists tend to inspect multiple windows for an accurate diagnosis, we
explicitly model this process and propose a multi-view feature pyramid network
(FPN), where multi-view features are extracted from images rendered with varied
window widths and window levels; to effectively combine this multi-view
information, we further propose a position-aware attention module. With the
proposed model design, the data-hunger problem is relieved as the learning task
is made easier with the correctly induced clinical practice prior. We show
promising results with the proposed model, achieving an absolute gain of
$\mathbf{5.65\%}$ (in the sensitivity of<EMAIL_ADDRESS>over the previous
state-of-the-art on the NIH DeepLesion dataset.
