  In this study, we propose a lung nodule detection scheme which fully
incorporates the clinic workflow of radiologists. Particularly, we exploit
Bi-Directional Maximum intensity projection (MIP) images of various thicknesses
(i.e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10
adjacent slices to feed into self-distillation-based Multi-Encoders Network
(MEDS-Net). The proposed architecture first condenses 3D patch input to three
channels by using a dense block which consists of dense units which effectively
examine the nodule presence from 2D axial slices. This condensed information,
along with the forward and backward MIP images, is fed to three different
encoders to learn the most meaningful representation, which is forwarded into
the decoded block at various levels. At the decoder block, we employ a
self-distillation mechanism by connecting the distillation block, which
contains five lung nodule detectors. It helps to expedite the convergence and
improves the learning ability of the proposed architecture. Finally, the
proposed scheme reduces the false positives by complementing the main detector
with auxiliary detectors. The proposed scheme has been rigorously evaluated on
888 scans of LUNA16 dataset and obtained a CPM score of 93.6\%. The results
demonstrate that incorporating of bi-direction MIP images enables MEDS-Net to
effectively distinguish nodules from surroundings which help to achieve the
sensitivity of 91.5% and 92.8% with false positives rate of 0.25 and 0.5 per
scan, respectively.
