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

arXiv: https://arxiv.org/abs/1801.09555 · HTML: https://mlsanity.com/paper/1801.09555/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2018.

## Built on by (most cited first, 30 of 36)

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- [A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron](https://mlsanity.com/paper/2202.06372/) (2022)
- [SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching](https://mlsanity.com/paper/2104.05215/) (2021)
- [Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification](https://mlsanity.com/paper/2101.07429/) (2021)
- [Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection](https://mlsanity.com/paper/2104.08845/) (2021)
- [DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis](https://mlsanity.com/paper/2106.06744/) (2021)
- [Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment](https://mlsanity.com/paper/2104.05261/) (2021)
- [Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention](https://mlsanity.com/paper/2110.12372/) (2021)
- [Medical Artificial Intelligence for Early Detection of Lung Cancer: A Survey](https://mlsanity.com/paper/2410.14769/) (2024)
- [The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification](https://mlsanity.com/paper/2108.05386/) (2021)
- [Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning](https://mlsanity.com/paper/2203.16606/) (2022)
- [LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection](https://mlsanity.com/paper/2208.02122/) (2022)
- [CPLOYO: A Pulmonary Nodule Detection Model with Multi-Scale Feature Fusion and Nonlinear Feature Learning](https://mlsanity.com/paper/2503.10045/) (2025)
- [AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics](https://mlsanity.com/paper/2110.10332/) (2021)
- [SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention](https://mlsanity.com/paper/2303.03625/) (2023)
- [Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models](https://mlsanity.com/paper/2303.11177/) (2023)
- [Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Data](https://mlsanity.com/paper/2307.03575/) (2023)
- [Dual Skip Connections Minimize the False Positive Rate of Lung Nodule Detection in CT images](https://mlsanity.com/paper/2110.13036/) (2021)
- [Deep Learning in Healthcare: An In-Depth Analysis](https://mlsanity.com/paper/2302.10904/) (2023)
- [Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images](https://mlsanity.com/paper/2407.02625/) (2024)
- [A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncology](https://mlsanity.com/paper/2503.14933/) (2025)
- [M3Net: A Macro-to-Meso-to-Micro Clinical-inspired Hierarchical 3D Network for Pulmonary Nodule Classification](https://mlsanity.com/paper/2605.12570/) (2026)
- [Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation](https://mlsanity.com/paper/2102.10919/) (2021)
- [Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans](https://mlsanity.com/paper/2103.14537/) (2021)
- [MHSnet: Multi-head and Spatial Attention Network with False-Positive Reduction for Pulmonary Nodules Detection](https://mlsanity.com/paper/2201.13392/) (2022)
- [Gastrointestinal Polyps and Tumors Detection Based on Multi-scale Feature-fusion with WCE Sequences](https://mlsanity.com/paper/2204.01012/) (2022)
- [Adjacent Slice Feature Guided 2.5D Network for Pulmonary Nodule Segmentation](https://mlsanity.com/paper/2211.10597/) (2022)

## Abstract (arXiv)

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.footnotehttps://github.com/uci-cbcl/DeepLung.git