  Lung cancer is a global and dangerous disease, and its early detection is
crucial to reducing the risks of mortality. In this regard, it has been of
great interest in developing a computer-aided system for pulmonary nodules
detection as early as possible on thoracic CT scans. In general, a nodule
detection system involves two steps: (i) candidate nodule detection at a high
sensitivity, which captures many false positives and (ii) false positive
reduction from candidates. However, due to the high variation of nodule
morphological characteristics and the possibility of mistaking them for
neighboring organs, candidate nodule detection remains a challenge. In this
study, we propose a novel Multi-scale Gradual Integration Convolutional Neural
Network (MGI-CNN), designed with three main strategies: (1) to use multi-scale
inputs with different levels of contextual information, (2) to use abstract
information inherent in different input scales with gradual integration, and
(3) to learn multi-stream feature integration in an end-to-end manner. To
verify the efficacy of the proposed network, we conducted exhaustive
experiments on the LUNA16 challenge datasets by comparing the performance of
the proposed method with state-of-the-art methods in the literature. On two
candidate subsets of the LUNA16 dataset, i.e., V1 and V2, our method achieved
an average CPM of 0.908 (V1) and 0.942 (V2), outperforming comparable methods
by a large margin. Our MGI-CNN is implemented in Python using TensorFlow and
the source code is available from 'https://github.com/ku-milab/MGICNN.'
