  This paper focuses on a novel approach for false-positive reduction (FPR) of
nodule candidates in Computer-aided detection (CADe) systems following the
suspicious lesions detection stage. Contrary to typical decisions in medical
image analysis, the proposed approach considers input data not as a 2D or 3D
image, but rather as a point cloud, and uses deep learning models for point
clouds. We discovered that point cloud models require less memory and are
faster both in training and inference compared to traditional CNN 3D, they
achieve better performance and do not impose restrictions on the size of the
input image, i.e. no restrictions on the size of the nodule candidate. We
propose an algorithm for transforming 3D CT scan data to point cloud. In some
cases, the volume of the nodule candidate can be much smaller than the
surrounding context, for example, in the case of subpleural localization of the
nodule. Therefore, we developed an algorithm for sampling points from a point
cloud constructed from a 3D image of the candidate region. The algorithm is
able to guarantee the capture of both context and candidate information as part
of the point cloud of the nodule candidate. We designed and set up an
experiment in creating a dataset from an open LIDC-IDRI database for a feature
of the FPR task, and is herein described in detail. Data augmentation was
applied both to avoid overfitting and as an upsampling method. Experiments were
conducted with PointNet, PointNet++, and DGCNN. We show that the proposed
approach outperforms baseline CNN 3D models and resulted in 85.98 FROC versus
77.26 FROC for baseline models. We compare our algorithm with published SOTA
and demonstrate that even without significant modifications it works at the
appropriate performance level on LUNA2016 and shows SOTA on LIDC-IDRI.
