Towards automatic pulmonary nodule management in lung cancer screening with deep learning.

ABSTRACT: The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was tra

SUBMITTER: Ciompi F 

PROVIDER: S-EPMC5395959 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature