title: Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks.
abstract: We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D patches from differently oriented planes is extracted. The proposed architecture comprises multiple streams of 2-D ConvNets, for which the outputs are combined using a dedicated fusion method to get the final classification. Data augmentation and dropout are applied to avoid overfitting. On 888 scans of the publicly available LIDC-IDRI dataset, our method reaches high detection sensitivities of 85.4% and 90.1% at 1 and 4 false positives per scan, respectively. An additional evaluation on independent datasets from the ANODE09 challenge and DLCST is performed. We showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system.
authors.forename: Arnaud Arindra Adiyoso
authors.initials: AA
authors.lastname: Setio
authors.forename: Francesco
authors.initials: F
authors.lastname: Ciompi
authors.forename: Geert
authors.initials: G
authors.lastname: Litjens
authors.forename: Paul
authors.initials: P
authors.lastname: Gerke
authors.forename: Colin
authors.initials: C
authors.lastname: Jacobs
authors.forename: Sarah J
authors.initials: SJ
authors.lastname: van Riel
authors.forename: Mathilde Marie Winkler
authors.initials: MM
authors.lastname: Wille
authors.forename: Matiullah
authors.initials: M
authors.lastname: Naqibullah
authors.forename: Clara I
authors.initials: CI
authors.lastname: Sanchez
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
mesh_terms: D000465:Algorithms; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*