DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
| Autor: | Xiaohui Xie, Wei Fan, Chaochun Liu, Wentao Zhu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 030218 nuclear medicine & medical imaging Machine Learning (cs.LG) 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine Neural and Evolutionary Computing (cs.NE) Subnetwork Contextual image classification Artificial neural network business.industry Computer Science - Neural and Evolutionary Computing Nodule (medicine) Pattern recognition DUAL (cognitive architecture) Computer Science - Learning Path (graph theory) 020201 artificial intelligence & image processing Artificial intelligence Gradient boosting medicine.symptom business |
| Zdroj: | WACV |
| DOI: | 10.48550/arxiv.1801.09555 |
| Popis: | 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} Comment: 9 pages, 8 figures, IEEE WACV conference. arXiv admin note: substantial text overlap with arXiv:1709.05538 |
| Databáze: | OpenAIRE |
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