{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deeplung-deep-3d-dual-path-nets-for-automated","title":"DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification","arxiv_id":"1801.09555","date":"2018-01-25","proceeding":null,"authors":["Wentao Zhu","Chaochun Liu","Wei Fan","Xiaohui Xie"],"abstract":"In this work, we present a fully automated lung computed tomography (CT)\ncancer diagnosis system, DeepLung. DeepLung consists of two components, nodule\ndetection (identifying the locations of candidate nodules) and classification\n(classifying candidate nodules into benign or malignant). Considering the 3D\nnature of lung CT data and the compactness of dual path networks (DPN), two\ndeep 3D DPN are designed for nodule detection and classification respectively.\nSpecifically, a 3D Faster Regions with Convolutional Neural Net (R-CNN) is\ndesigned for nodule detection with 3D dual path blocks and a U-net-like\nencoder-decoder structure to effectively learn nodule features. For nodule\nclassification, gradient boosting machine (GBM) with 3D dual path network\nfeatures is proposed. The nodule classification subnetwork was validated on a\npublic dataset from LIDC-IDRI, on which it achieved better performance than\nstate-of-the-art approaches and surpassed the performance of experienced\ndoctors based on image modality. Within the DeepLung system, candidate nodules\nare detected first by the nodule detection subnetwork, and nodule diagnosis is\nconducted by the classification subnetwork. Extensive experimental results\ndemonstrate that DeepLung has performance comparable to experienced doctors\nboth for the nodule-level and patient-level diagnosis on the LIDC-IDRI\ndataset.\\footnote{https://github.com/uci-cbcl/DeepLung.git}","url_abs":"http://arxiv.org/abs/1801.09555v1","url_pdf":"http://arxiv.org/pdf/1801.09555v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deeplung-deep-3d-dual-path-nets-for-automated","repo_url":"https://github.com/uci-cbcl/DeepLung","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"deeplung-deep-3d-dual-path-nets-for-automated","repo_url":"https://github.com/2023-MindSpore-4/Code3/tree/main/dpn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"lung-nodule-classification","task_name":"Lung Nodule Classification"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dpn","method_name":"DPN"},{"method_slug":"dpn-block","method_name":"DPN Block"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"r-cnn","method_name":"R-CNN"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"svm","method_name":"SVM"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lung-nodule-classification-on-lidc-idri","task":"Lung Nodule Classification","dataset":"LIDC-IDRI","model":"DeepLung","rank_in_archive_order":5,"of":8,"metrics":{"Acc":"90.44","Accuracy":"90.44"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}