{"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/a-systematic-analysis-for-state-of-the-art-3d","title":"A Systematic Analysis for State-of-the-Art 3D Lung Nodule Proposals Generation","arxiv_id":"1802.02179","date":"2018-01-09","proceeding":null,"authors":["Hui Wu","Matrix Yao","Albert Hu","Gaofeng Sun","Xiaokun Yu","Jian Tang"],"abstract":"Lung nodule proposals generation is the primary step of lung nodule detection\nand has received much attention in recent years . In this paper, we first\nconstruct a model of 3-dimension Convolutional Neural Network (3D CNN) to\ngenerate lung nodule proposals, which can achieve the state-of-the-art\nperformance. Then, we analyze a series of key problems concerning the training\nperformance and efficiency. Firstly, we train the 3D CNN model with data in\ndifferent resolutions and find out that models trained by high resolution input\ndata achieve better lung nodule proposals generation performances especially\nfor nodules in too small sizes, while consumes much more memory at the same\ntime. Then, we analyze the memory consumptions on different platforms and the\nexperimental results indicate that CPU architecture can provide us with larger\nmemory and enables us to explore more possibilities of 3D applications. We\nimplement the 3D CNN model on CPU platform and propose an Intel Extended-Caffe\nframework which supports many highly-efficient 3D computations, which is opened\nsource at https://github.com/extendedcaffe/extended-caffe.","url_abs":"http://arxiv.org/abs/1802.02179v1","url_pdf":"http://arxiv.org/pdf/1802.02179v1.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":"a-systematic-analysis-for-state-of-the-art-3d","repo_url":"https://github.com/extendedcaffe/extended-caffe","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"lung-nodule-detection","task_name":"Lung Nodule Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}