Risk Stratification Of Lung Nodules Using 3D Cnn-Based Multi-Task Learning

Keywords

3D convolutional neural network; Computed tomography (CT); Computer-aided diagnosis (CAD); Deep learning; Lung nodule characterization; Multi-task learning; Transfer learning

Abstract

Risk stratification of lung nodules is a task of primary importance in lung cancer diagnosis. Any improvement in robust and accurate nodule characterization can assist in identifying cancer stage, prognosis, and improving treatment planning. In this study, we propose a 3D Convolutional Neural Network (CNN) based nodule characterization strategy. With a completely 3D approach, we utilize the volumetric information from a CT scan which would be otherwise lost in the conventional 2D CNN based approaches. In order to address the need for a large amount of training data for CNN, we resort to transfer learning to obtain highly discriminative features.Moreover, we also acquire the task dependent feature representation for six high-level nodule attributes and fuse this complementary information via a Multi-task learning (MTL) framework. Finally, we propose to incorporate potential disagreement among radiologists while scoring different nodule attributes in a graph regularized sparsemulti-task learning. We evaluated our proposed approach on one of the largest publicly available lung nodule datasets comprising 1018 scans and obtained state-of-the-art results in regressing the malignancy scores.

Publication Date

1-1-2017

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

10265 LNCS

Number of Pages

249-260

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-319-59050-9_20

Socpus ID

85020530877 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85020530877

This document is currently not available here.

Share

COinS