DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification

• Published in: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) pp. 673 - 681
• Main Authors: Zhu, Wentao, Liu, Chaochun, Fan, Wei, Xie, Xiaohui
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2018
• Subjects: Cancer; Computed tomography; Feature extraction; Lung; Neural networks; Three-dimensional displays
• DOI: 10.1109/WACV.2018.00079

In this work, we present a fully automated lung computed tomography (CT) cancer diagnosis system, DeepLung. DeepLung consists of two components, nodule detection (identifying the locations of candidate nodules) and classification (classifying candidate nodules into benign or malignant). Considering the 3D nature of lung CT data and the compactness of dual path networks (DPN), two deep 3D DPN are designed for nodule detection and classification respectively. Specifically, a 3D Faster Regions with Convolutional Neural Net (R-CNN) is designed for nodule detection with 3D dual path blocks and a U-net-like encoder-decoder structure to effectively learn nodule features. For nodule classification, gradient boosting machine (GBM) with 3D dual path network features is proposed. The nodule classification subnetwork was validated on a public dataset from LIDC-IDRI, on which it achieved better performance than state-of-the-art approaches and surpassed the performance of experienced doctors based on image modality. Within the DeepLung system, candidate nodules are detected first by the nodule detection subnetwork, and nodule diagnosis is conducted by the classification subnetwork. Extensive experimental results demonstrate that DeepLung has performance comparable to experienced doctors both for the nodule-level and patient-level diagnosis on the LIDC-IDRI dataset.