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

TL;DR

DeepLung is an automated system using 3D dual path networks for lung nodule detection and classification in CT scans, achieving performance comparable to experienced doctors.

Contribution

It introduces a novel 3D dual path network architecture for both nodule detection and classification, surpassing state-of-the-art methods and outperforming doctors on a public dataset.

Findings

DeepLung achieves high accuracy in nodule detection and classification.

The system outperforms existing methods and matches experienced doctors.

Experimental results validate its effectiveness on the LIDC-IDRI dataset.

Abstract

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.\footnote{https://github.com/uci-cbcl/DeepLung.git}