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

By Wentao Zhu, Chaochun Liu, Wei Fan, Xiaohui S. Xie. IEEE Workshop/Winter Conference on Applications of Computer Vision, 2018

429 citations recorded by the source corpus.

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.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ef060acba2fbe9704efdc470ac4a2f1049034de3

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In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Hybrid deep learning for detecting lung diseases from X-ray images (2020, methodology): “Many studies [10-31] propose many deep learning models in order to detect lung cancer and other lung diseases.”
AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy (2018, methodology): “The AnatomyNet is a variant of 3D U-Net [25, 38, 39], one of the most commonly used neural net architectures in biomedical image segmentation.”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, background): “, brain tumor [35] and lung nodules [36].”
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images (2018, neutral): “…classification of Alzheimer’s disease (Sarraf et al., 2017), skin lesions (Esteva et al., 2017; Kawahara and Hamarneh, 2016) and echo-cardiogram views (Madani et al., 2018), lung nodule detection in CT/X-ray (Liao et al., 2017; Zhu et al., 2018) and cardiac MR segmentation (Bai et al., 2017).”
Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies (2019, methodology): “proposed automated lung nodule detection and classification models using 3D DPN with 3D faster R-CNN and gradient boosting machine (GBM) by learning spatial features of lung nodules [18].”
Development and evaluation of an artificial intelligence system for COVID-19 diagnosis (2020, positive): “Comparing to other lung diseases, such as lung nodule detection 18 – 20 , tuberculosis diagnosis 16,21 , and lung cancer screening 15 , differentiating COVID-19 from other pneumonias has unique dif ﬁ culty, i.e., high similarity of pneumonias of different types (especially in early stage) and large…”