{"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/detecting-lesion-bounding-ellipses-with","title":"Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks","arxiv_id":"1902.09658","date":"2019-02-25","proceeding":null,"authors":["Yi Li"],"abstract":"Lesions characterized by computed tomography (CT) scans, are arguably often\nelliptical objects. However, current lesion detection systems are predominantly\nadopted from the popular Region Proposal Networks (RPNs) that only propose\nbounding boxes without fully leveraging the elliptical geometry of lesions. In\nthis paper, we present Gaussian Proposal Networks (GPNs), a novel extension to\nRPNs, to detect lesion bounding ellipses. Instead of directly regressing the\nrotation angle of the ellipse as the common practice, GPN represents bounding\nellipses as 2D Gaussian distributions on the image plain and minimizes the\nKullback-Leibler (KL) divergence between the proposed Gaussian and the ground\ntruth Gaussian for object localization. We show the KL divergence loss\napproximately incarnates the regression loss in the RPN framework when the\nrotation angle is 0. Experiments on the DeepLesion dataset show that GPN\nsignificantly outperforms RPN for lesion bounding ellipse detection thanks to\nlower localization error. GPN is open sourced at\nhttps://github.com/baidu-research/GPN","url_abs":"http://arxiv.org/abs/1902.09658v1","url_pdf":"http://arxiv.org/pdf/1902.09658v1.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":"detecting-lesion-bounding-ellipses-with","repo_url":"https://github.com/baidu-research/GPN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"lesion-detection","task_name":"Lesion Detection"},{"task_slug":"object-localization","task_name":"Object Localization"},{"task_slug":"region-proposal","task_name":"Region Proposal"}],"methods":[{"method_slug":"rpn","method_name":"RPN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.09658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09658"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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