Tumornet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network With Gaussian Process

Keywords

Computed tomography; Computer-aided diagnosis; Deep learning; Lung cancer; Pulmonary nodule

Abstract

Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for nodule characterization. First, we use median intensity projection to obtain a 2D patch corresponding to each dimension. The three images are then concatenated to form a tensor, where the images serve as different channels of the input image. In order to increase the number of training samples, we perform data augmentation by scaling, rotating and adding noise to the input image. The trained network is used to extract features from the input image followed by a Gaussian Process (GP) regression to obtain the malignancy score. We also empirically establish the significance of different high level nodule attributes such as calcification, sphericity and others for malignancy determination. These attributes are found to be complementary to the deep multi-view CNN features and a significant improvement over other methods is obtained.

Publication Date

6-15-2017

Publication Title

Proceedings - International Symposium on Biomedical Imaging

Number of Pages

1007-1010

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ISBI.2017.7950686

Socpus ID

85023179234 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85023179234

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