  Pulmonary nodule detection using low-dose Computed Tomography (CT) is often
the first step in lung disease screening and diagnosis. Recently, algorithms
based on deep convolutional neural nets have shown great promise for automated
nodule detection. Most of the existing deep learning nodule detection systems
are constructed in two steps: a) nodule candidates screening and b) false
positive reduction, using two different models trained separately. Although it
is commonly adopted, the two-step approach not only imposes significant
resource overhead on training two independent deep learning models, but also is
sub-optimal because it prevents cross-talk between the two. In this work, we
present an end-to-end framework for nodule detection, integrating nodule
candidate screening and false positive reduction into one model, trained
jointly. We demonstrate that the end-to-end system improves the performance by
3.88\% over the two-step approach, while at the same time reducing model
complexity by one third and cutting inference time by 3.6 fold. Code will be
made publicly available.
